NOTE: This includes all the set-up and processing for the CLSA Comprehensive cohort data
if (!require("pacman")) install.packages("pacman")
## Loading required package: pacman
pacman::p_load(MASS,plyr,ggplot2,lme4,nlme, rms,dplyr, lubridate, effects,
lmerTest,rpart,tableone,psych,Hmisc,magrittr, ggeffects,sjmisc,splines,
lsmeans,openxlsx)
set.seed(1)
setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#Baseline (Non-Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_BL")
trackBL<-read.csv("23SP001_McMaster_PRaina_Baseline_Trav4.csv")#Comprehensive Cohort Baseline
track.BL<-trackBL[c(1:5,104,926,798,106,892,921,32,1363,1377,1378,1379,1380,1407,1408,1433,1466,1432,1465,
1480,1481:1488,893,928,1093,1094,1096,1125,1124,376,1086,1089,1097:1102,1105,1106,
1129,1132,1133,1077,1076,1075,1078,1079,1085,1082,1130,1087,1090,1088,1131,1091,1080,
1128,1095,1081,334)]
#Sex
track.BL$Sex<-track.BL$SEX_ASK_TRM
#Age
track.BL$Age<-track.BL$AGE_NMBR_TRM
#Marital Status
track.BL$Relationship_status<-NA
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==1]<-"Single"
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==2]<-"Married"
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==3]<-"Widowed"
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==4]<-"Divorced"
track.BL$Relationship_status[track.BL$SDC_MRTL_TRM==5]<-"Separated"
#Education 4 Category
track.BL$Education4<-NA
track.BL$Education4[track.BL$ED_UDR04_TRM==1]<-"Less than High School Diploma"
track.BL$Education4[track.BL$ED_UDR04_TRM==2]<-"High School Diploma"
track.BL$Education4[track.BL$ED_UDR04_TRM==3]<-"Some College"
track.BL$Education4[track.BL$ED_UDR04_TRM==4]<-"College Degree or Higher"
#Household Income
track.BL$Income_Level<-NA
track.BL$Income_Level[track.BL$INC_PTOT_TRM==1]<-"<$20k"
track.BL$Income_Level[track.BL$INC_PTOT_TRM==2]<-"$20-50k"
track.BL$Income_Level[track.BL$INC_PTOT_TRM==3]<-"$50-100k"
track.BL$Income_Level[track.BL$INC_PTOT_TRM==4]<-"$100-150k"
track.BL$Income_Level[track.BL$INC_PTOT_TRM==5]<-">$150k"
#Living Status
track.BL$Living_status<-NA
track.BL$Living_status[track.BL$OWN_DWLG_TRM==1]<-"House"
track.BL$Living_status[track.BL$OWN_DWLG_TRM==2 |track.BL$OWN_DWLG_TRM==6]<-"Apartment/Condo/Townhome"
track.BL$Living_status[track.BL$OWN_DWLG_TRM==3]<-"Assisted Living"
track.BL$Living_status[track.BL$OWN_DWLG_TRM==4 | track.BL$OWN_DWLG_TRM==5 | track.BL$OWN_DWLG_TRM>=7]<-"Other"
#Alcohol
track.BL$Alcohol<-NA
track.BL$Alcohol[track.BL$ALC_TTM_TRM==1]<-"Regular drinker (at least once a month)"
track.BL$Alcohol[track.BL$ALC_TTM_TRM==2]<-"Occasional drinker"
track.BL$Alcohol[track.BL$ALC_TTM_TRM==3]<-"Non-drinker"
#Smoking Status
track.BL$Smoking_Status<-NA
track.BL$Smoking_Status[track.BL$SMK_DSTY_TRM==1]<-"Daily Smoker"
track.BL$Smoking_Status[track.BL$SMK_DSTY_TRM==2 |track.BL$SMK_DSTY_TRM==3]<-"Occasional Smoker"
track.BL$Smoking_Status[track.BL$SMK_DSTY_TRM==4 | track.BL$SMK_DSTY_TRM==5]<-"Former Smoker"
track.BL$Smoking_Status[track.BL$SMK_DSTY_TRM==6]<-"Never Smoked"
track.BL$Ethnicity<-NA
track.BL$Ethnicity[track.BL$SDC_CULT_WH_TRM==1]<-"White"
track.BL$Ethnicity[track.BL$SDC_CULT_WH_TRM==0]<-"Other"
#############Physical Activity Scale for the Elderly######################
#Q1: Sitting Activity Frequency in Past 7 days
track.BL$PASE_Q1<-NA
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ==1]<-0
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ==2]<-0.11
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ==3]<-0.25
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ==4]<-0.43
track.BL$PASE_Q1[track.BL$PA2_SIT_MCQ>4]<-NA
track.BL$PASE_Q1[is.na(track.BL$PA2_SIT_MCQ)]<-NA
track.BL$PASE_Q1B<-NA
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==1]<-0
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==2]<-1
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==3]<-3
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==4]<-6
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ==5]<-10
track.BL$PASE_Q1B[track.BL$PA2_SITHR_MCQ>5]<- 0
track.BL$PASE_Q1B[is.na(track.BL$PA2_SITHR_MCQ)]<-NA
track.BL$PASE_Q1B<-as.numeric(track.BL$PASE_Q1B)
#Q2: Walking outside frequency
track.BL$PASE_Q2<- NULL
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ==1]<-0
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ==2]<-0.11
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ==3]<-0.25
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ==4]<-0.43
track.BL$PASE_Q2[track.BL$PA2_WALK_MCQ>4]<-NA
track.BL$PASE_Q2[is.na(track.BL$PA2_WALK_MCQ)]<-NA
track.BL$PASE_Q2A<- NA
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==1]<-0
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==2]<-1
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==3]<-3
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==4]<-6
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ==5]<-10
track.BL$PASE_Q2A[track.BL$PA2_WALKHR_MCQ>5]<-NA
track.BL$PASE_Q2A[is.na(track.BL$PA2_WALKHR_MCQ)]<-NA
track.BL$PASE_Q2A<-as.numeric(track.BL$PASE_Q2A)
#Q3: Light Sports or Activity Frequency
track.BL$PASE_Q3<- NA
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ==1]<-0
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ==2]<-0.11
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ==3]<-0.25
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ==4]<-0.43
track.BL$PASE_Q3[track.BL$PA2_LSPRT_MCQ>4]<-NA
track.BL$PASE_Q3[is.na(track.BL$PA2_LSPRT_MCQ)]<-NA
track.BL$PASE_Q3A<- NA
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==1]<-0
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==2]<-1
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==3]<-3
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==4]<-6
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ==5]<-10
track.BL$PASE_Q3A[track.BL$PA2_LSPRTHR_MCQ>5]<-NA
track.BL$PASE_Q3A[track.BL$PA2_LSPRT_MCQ == 1 | is.na(track.BL$PA2_LSPRTHR_MCQ)]<-0
track.BL$PASE_Q3A[is.na(track.BL$PA2_LSPRT_MCQ) & is.na(track.BL$PA2_LSPRTHR_MCQ)]<-NA
track.BL$PASE_Q3A[track.BL$PA2_LSPRT_MCQ>4 & is.na(track.BL$PA2_LSPRTHR_MCQ)]<-NA
track.BL$PASE_Q3A<-as.numeric(track.BL$PASE_Q3A)
#Q4: Moderate Sports or Activity Frequency
track.BL$PASE_Q4<-NA
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ==1]<-0
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ==2]<-0.11
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ==3]<-0.25
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ==4]<-0.43
track.BL$PASE_Q4[track.BL$PA2_MSPRT_MCQ>4]<-NA
track.BL$PASE_Q4[is.na(track.BL$PA2_MSPRT_MCQ)]<-NA
track.BL$PASE_Q4A<- NA
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==1]<-0
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==2]<-1
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==3]<-3
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==4]<-6
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ==5]<-10
track.BL$PASE_Q4A[track.BL$PA2_MSPRTHR_MCQ>5]<-NA
track.BL$PASE_Q4A[track.BL$PA2_MSPRT_MCQ == 1 | is.na(track.BL$PA2_MSPRTHR_MCQ)]<-0
track.BL$PASE_Q4A[is.na(track.BL$PA2_MSPRT_MCQ) & is.na(track.BL$PA2_MSPRTHR_MCQ)]<-NA
track.BL$PASE_Q4A[track.BL$PA2_MSPRT_MCQ>4 & is.na(track.BL$PA2_MSPRTHR_MCQ)]<-NA
track.BL$PASE_Q4A<-as.numeric(track.BL$PASE_Q4A)
#Q5: Strenuous Sports or Activity Frequency
track.BL$PASE_Q5<-NA
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ==1]<-0
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ==2]<-0.11
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ==3]<-0.25
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ==4]<-0.43
track.BL$PASE_Q5[track.BL$PA2_SSPRT_MCQ>4]<-NA
track.BL$PASE_Q5[is.na(track.BL$PA2_SSPRT_MCQ)]<-NA
track.BL$PASE_Q5A<- NA
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==1]<-0
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==2]<-1
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==3]<-3
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==4]<-6
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ==5]<-10
track.BL$PASE_Q5A[track.BL$PA2_SSPRTHR_MCQ>5]<-NA
track.BL$PASE_Q5A[track.BL$PA2_SSPRT_MCQ == 1 | is.na(track.BL$PA2_SSPRTHR_MCQ)]<-0
track.BL$PASE_Q5A[is.na(track.BL$PA2_SSPRT_MCQ) & is.na(track.BL$PA2_SSPRTHR_MCQ)]<-NA
track.BL$PASE_Q5A[track.BL$PA2_SSPRT_MCQ>4 & is.na(track.BL$PA2_SSPRTHR_MCQ)]<-NA
track.BL$PASE_Q5A<-as.numeric(track.BL$PASE_Q5A)
#Q6: Muscle strengthening and endurance exercise
track.BL$PASE_Q6<-NA
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ==1]<-0
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ==2]<-0.11
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ==3]<-0.25
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ==4]<-0.43
track.BL$PASE_Q6[track.BL$PA2_EXER_MCQ>4]<-NA
track.BL$PASE_Q6[is.na(track.BL$PA2_EXER_MCQ)]<-NA
track.BL$PASE_Q6A<- NA
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==1]<-0
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==2]<-1
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==3]<-3
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==4]<-6
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ==5]<-10
track.BL$PASE_Q6A[track.BL$PA2_EXERHR_MCQ>5]<-NA
track.BL$PASE_Q6A[track.BL$PA2_EXER_MCQ == 1 | is.na(track.BL$PA2_EXERHR_MCQ)]<-0
track.BL$PASE_Q6A[is.na(track.BL$PA2_EXER_MCQ) & is.na(track.BL$PA2_EXERHR_MCQ)]<-NA
track.BL$PASE_Q6A[track.BL$PA2_EXER_MCQ>4 & is.na(track.BL$PA2_EXERHR_MCQ)]<-NA
track.BL$PASE_Q6A<-as.numeric(track.BL$PASE_Q6A)
#Q7: Light Housework
track.BL$PASE_Q7<-NA
track.BL$PASE_Q7[track.BL$PA2_LTHSWK_MCQ==1]<-1
track.BL$PASE_Q7[track.BL$PA2_LTHSWK_MCQ==2]<-0
track.BL$PASE_Q7[track.BL$PA2_LTHSWK_MCQ>2]<-NA
track.BL$PASE_Q7[is.na(track.BL$PA2_LTHSWK_MCQ)]<-NA
track.BL$PASE_Q7<-as.numeric(track.BL$PASE_Q7)
#Q8: Heavy Housework
track.BL$PASE_Q8<-NA
track.BL$PASE_Q8[track.BL$PA2_HVYHSWK_MCQ==1]<-1
track.BL$PASE_Q8[track.BL$PA2_HVYHSWK_MCQ==2]<-0
track.BL$PASE_Q8[track.BL$PA2_HVYHSWK_MCQ>2]<-NA
track.BL$PASE_Q8[is.na(track.BL$PA2_HVYHSWK_MCQ)]<-NA
track.BL$PASE_Q8<-as.numeric(track.BL$PASE_Q8)
#Q9: Home Repair, Yardwork, Gardening, Care for another person
track.BL$PASE_Q9A<-NA
track.BL$PASE_Q9A[track.BL$PA2_HMREPAIR_MCQ==1]<-1
track.BL$PASE_Q9A[track.BL$PA2_HMREPAIR_MCQ==2]<-0
track.BL$PASE_Q9A[track.BL$PA2_HMREPAIR_MCQ>2]<-NA
track.BL$PASE_Q9A[is.na(track.BL$PA2_HMREPAIR_MCQ)]<-NA
track.BL$PASE_Q9A<-as.numeric(track.BL$PASE_Q9A)
track.BL$PASE_Q9B<-NA
track.BL$PASE_Q9B[track.BL$PA2_HVYODA_MCQ==1]<-1
track.BL$PASE_Q9B[track.BL$PA2_HVYODA_MCQ==2]<-0
track.BL$PASE_Q9B[track.BL$PA2_HVYODA_MCQ>2]<-NA
track.BL$PASE_Q9B[is.na(track.BL$PA2_HVYODA_MCQ)]<-NA
track.BL$PASE_Q9B<-as.numeric(track.BL$PASE_Q9B)
track.BL$PASE_Q9C<-NA
track.BL$PASE_Q9C[track.BL$PA2_LTODA_MCQ==1]<-1
track.BL$PASE_Q9C[track.BL$PA2_LTODA_MCQ==2]<-0
track.BL$PASE_Q9C[track.BL$PA2_LTODA_MCQ>2]<-NA
track.BL$PASE_Q9C[is.na(track.BL$PA2_LTODA_MCQ)]<-NA
track.BL$PASE_Q9C<-as.numeric(track.BL$PASE_Q9C)
track.BL$PASE_Q9D<-NA
track.BL$PASE_Q9D[track.BL$PA2_CRPRSN_MCQ==1]<-1
track.BL$PASE_Q9D[track.BL$PA2_CRPRSN_MCQ==2]<-0
track.BL$PASE_Q9D[track.BL$PA2_CRPRSN_MCQ>2]<-NA
track.BL$PASE_Q9D[is.na(track.BL$PA2_CRPRSN_MCQ)]<-NA
track.BL$PASE_Q9D<-as.numeric(track.BL$PASE_Q9D)
#Q10: Working and Volunteering
track.BL$PASE_Q10<-NA
track.BL$PASE_Q10[track.BL$PA2_WRK_MCQ==1]<-1
track.BL$PASE_Q10[track.BL$PA2_WRK_MCQ==2]<-0
track.BL$PASE_Q10[track.BL$PA2_WRK_MCQ>2]<-NA
track.BL$PASE_Q10[is.na(track.BL$PA2_WRK_MCQ)]<-NA
track.BL$PASE_Q10<-as.numeric(track.BL$PASE_Q10)
track.BL$PASE_Q10A<-NA
track.BL$PASE_Q10A<-track.BL$PA2_WRKHRS_NB_MCQ
track.BL$PASE_Q10A[track.BL$PA2_WRKHRS_NB_MCQ>=700]<-NA
track.BL$PASE_Q10A[track.BL$PA2_WRK_MCQ == 2 | is.na(track.BL$PA2_WRKHRS_NB_MCQ)]<-0
track.BL$PASE_Q10A[is.na(track.BL$PA2_WRK_MCQ) & is.na(track.BL$PA2_WRKHRS_NB_MCQ)]<-NA
track.BL$PASE_Q10A<-as.numeric(track.BL$PASE_Q10A)
track.BL$PASE_Q10A<-track.BL$PASE_Q10A/7
#PASE TOTAL SCORE#
track.BL$PASE_TOTAL<-track.BL$PASE_Q2*track.BL$PASE_Q2A*20 + track.BL$PASE_Q3*track.BL$PASE_Q3A*21 + track.BL$PASE_Q4*track.BL$PASE_Q4A*23 + track.BL$PASE_Q5*track.BL$PASE_Q5A*30 +
track.BL$PASE_Q6*track.BL$PASE_Q6A*30 + (track.BL$PASE_Q7+track.BL$PASE_Q8)*25 + track.BL$PASE_Q9A*30 + track.BL$PASE_Q9B*36 + track.BL$PASE_Q9C*20 + track.BL$PASE_Q9D*35 +
track.BL$PASE_Q10*track.BL$PASE_Q10A*21
#BMI
track.BL$BMI<-track.BL$HWT_DBMI_TRM
track.BL$BMI[track.BL$HWT_DBMI_TRM>100]<-NA
#CESD-10
track.BL$CESD_10<-track.BL$DEP_CESD10_TRM
track.BL$CESD_10[track.BL$DEP_CESD10_TRM>50]<-NA
track.BL$CESD_10[track.BL$DEP_CESD10_TRM==-88]<-NA
#Subjective Cognitive Impairment
track.BL$SCI<- NA
track.BL$SCI[track.BL$CCT_MEMPB_TRM==1]<- "Yes"
track.BL$SCI[track.BL$CCT_MEMPB_TRM==2]<- "No"
#Dementia and AD
track.BL$Dementia<- NA
track.BL$Dementia[track.BL$CCT_ALZH_TRM==1]<- "Yes"
track.BL$Dementia[track.BL$CCT_ALZH_TRM==2]<- "No"
#Anxiety
track.BL$Anxiety<- NA
track.BL$Anxiety[track.BL$CCT_ANXI_TRM==1]<- "Yes"
track.BL$Anxiety[track.BL$CCT_ANXI_TRM==2]<- "No"
#Mood Disorders
track.BL$Mood_Disord<- NA
track.BL$Mood_Disord[track.BL$CCT_MOOD_TRM==1]<- "Yes"
track.BL$Mood_Disord[track.BL$CCT_MOOD_TRM==2]<- "No"
#Pet Ownership at Baseline
track.BL$Pet_Owner<-NA
track.BL$Pet_Owner[track.BL$SSA_PET_TRM==1]<-"Yes"
track.BL$Pet_Owner[track.BL$SSA_PET_TRM==2]<-"No"
#Number of Chronic Conditions
track.BL$Chronic_conditions<-NA
track.BL$CCT_HEART_TRM[track.BL$CCT_HEART_TRM==1]<- 1 #Heart Disease
track.BL$CCT_HEART_TRM[track.BL$CCT_HEART_TRM==2]<- 0
track.BL$CCT_HEART_TRM[track.BL$CCT_HEART_TRM==8]<- NA
track.BL$CCT_HEART_TRM[track.BL$CCT_HEART_TRM==9]<- NA
track.BL$CCT_PVD_TRM[track.BL$CCT_PVD_TRM==1]<- 1 #peripheral vascular disease
track.BL$CCT_PVD_TRM[track.BL$CCT_PVD_TRM==2]<- 0
track.BL$CCT_PVD_TRM[track.BL$CCT_PVD_TRM==8]<- NA
track.BL$CCT_PVD_TRM[track.BL$CCT_PVD_TRM==9]<- NA
track.BL$CCT_MEMPB_TRM[track.BL$CCT_MEMPB_TRM==1]<- 1 #SCI
track.BL$CCT_MEMPB_TRM[track.BL$CCT_MEMPB_TRM==2]<- 0
track.BL$CCT_MEMPB_TRM[track.BL$CCT_MEMPB_TRM==8]<- NA
track.BL$CCT_MEMPB_TRM[track.BL$CCT_MEMPB_TRM==9]<- NA
track.BL$CCT_ALZH_TRM[track.BL$CCT_ALZH_TRM==1]<- 1 #Alzheimers or demeinta
track.BL$CCT_ALZH_TRM[track.BL$CCT_ALZH_TRM==2]<- 0
track.BL$CCT_ALZH_TRM[track.BL$CCT_ALZH_TRM==8]<- NA
track.BL$CCT_ALZH_TRM[track.BL$CCT_ALZH_TRM==9]<- NA
track.BL$CCT_MS_TRM[track.BL$CCT_MS_TRM==1]<- 1 #Multiple sclerosis
track.BL$CCT_MS_TRM[track.BL$CCT_MS_TRM==2]<- 0
track.BL$CCT_MS_TRM[track.BL$CCT_MS_TRM==8]<- NA
track.BL$CCT_MS_TRM[track.BL$CCT_MS_TRM==9]<- NA
track.BL$CCT_EPIL_TRM[track.BL$CCT_EPIL_TRM==1]<- 1 #Epilepsy
track.BL$CCT_EPIL_TRM[track.BL$CCT_EPIL_TRM==2]<- 0
track.BL$CCT_EPIL_TRM[track.BL$CCT_EPIL_TRM==8]<- NA
track.BL$CCT_EPIL_TRM[track.BL$CCT_EPIL_TRM==9]<- NA
track.BL$CCT_MGRN_TRM[track.BL$CCT_MGRN_TRM==1]<- 1 #Migraine headaches
track.BL$CCT_MGRN_TRM[track.BL$CCT_MGRN_TRM==2]<- 0
track.BL$CCT_MGRN_TRM[track.BL$CCT_MGRN_TRM==8]<- NA
track.BL$CCT_MGRN_TRM[track.BL$CCT_MGRN_TRM==9]<- NA
track.BL$CCT_ULCR_TRM[track.BL$CCT_ULCR_TRM==1]<- 1 #Intenstinal or stomach ulcers
track.BL$CCT_ULCR_TRM[track.BL$CCT_ULCR_TRM==2]<- 0
track.BL$CCT_ULCR_TRM[track.BL$CCT_ULCR_TRM==8]<- NA
track.BL$CCT_ULCR_TRM[track.BL$CCT_ULCR_TRM==9]<- NA
track.BL$CCT_IBDIBS_TRM[track.BL$CCT_IBDIBS_TRM==1]<- 1 #Bowel disorder
track.BL$CCT_IBDIBS_TRM[track.BL$CCT_IBDIBS_TRM==2]<- 0
track.BL$CCT_IBDIBS_TRM[track.BL$CCT_IBDIBS_TRM==8]<- NA
track.BL$CCT_IBDIBS_TRM[track.BL$CCT_IBDIBS_TRM==9]<- NA
track.BL$CCT_BOWINC_TRM[track.BL$CCT_BOWINC_TRM==1]<- 1 #Bowel incontinence
track.BL$CCT_BOWINC_TRM[track.BL$CCT_BOWINC_TRM==2]<- 0
track.BL$CCT_BOWINC_TRM[track.BL$CCT_BOWINC_TRM==8]<- NA
track.BL$CCT_BOWINC_TRM[track.BL$CCT_BOWINC_TRM==9]<- NA
track.BL$CCT_URIINC_TRM[track.BL$CCT_URIINC_TRM==1]<- 1 #Urinary incontinence
track.BL$CCT_URIINC_TRM[track.BL$CCT_URIINC_TRM==2]<- 0
track.BL$CCT_URIINC_TRM[track.BL$CCT_URIINC_TRM==8]<- NA
track.BL$CCT_URIINC_TRM[track.BL$CCT_URIINC_TRM==9]<- NA
track.BL$CCT_MACDEG_TRM[track.BL$CCT_MACDEG_TRM==1]<- 1 #Macular degeneration
track.BL$CCT_MACDEG_TRM[track.BL$CCT_MACDEG_TRM==2]<- 0
track.BL$CCT_MACDEG_TRM[track.BL$CCT_MACDEG_TRM==8]<- NA
track.BL$CCT_MACDEG_TRM[track.BL$CCT_MACDEG_TRM==9]<- NA
track.BL$CCT_CANC_TRM[track.BL$CCT_CANC_TRM==1]<- 1 #All-cause cancer
track.BL$CCT_CANC_TRM[track.BL$CCT_CANC_TRM==2]<- 0
track.BL$CCT_CANC_TRM[track.BL$CCT_CANC_TRM==8]<- NA
track.BL$CCT_CANC_TRM[track.BL$CCT_CANC_TRM==9]<- NA
track.BL$CCT_BCKP_TRM[track.BL$CCT_BCKP_TRM==1]<- 1 #Back problems but not fibromyalgia or arthritis
track.BL$CCT_BCKP_TRM[track.BL$CCT_BCKP_TRM==2]<- 0
track.BL$CCT_BCKP_TRM[track.BL$CCT_BCKP_TRM==8]<- NA
track.BL$CCT_BCKP_TRM[track.BL$CCT_BCKP_TRM==9]<- NA
track.BL$CCT_KIDN_TRM[track.BL$CCT_KIDN_TRM==1]<- 1 #Kidney disease
track.BL$CCT_KIDN_TRM[track.BL$CCT_KIDN_TRM==2]<- 0
track.BL$CCT_KIDN_TRM[track.BL$CCT_KIDN_TRM==8]<- NA
track.BL$CCT_KIDN_TRM[track.BL$CCT_KIDN_TRM==9]<- NA
track.BL$CCT_OTCCT_TRM[track.BL$CCT_OTCCT_TRM==1]<- 1 #Other long term mental or physical condition
track.BL$CCT_OTCCT_TRM[track.BL$CCT_OTCCT_TRM==2]<- 0
track.BL$CCT_OTCCT_TRM[track.BL$CCT_OTCCT_TRM==8]<- NA
track.BL$CCT_OTCCT_TRM[track.BL$CCT_OTCCT_TRM==9]<- NA
track.BL$CCT_OAHAND_TRM[track.BL$CCT_OAHAND_TRM==1]<- 1 #Hand arthritis
track.BL$CCT_OAHAND_TRM[track.BL$CCT_OAHAND_TRM==2]<- 0
track.BL$CCT_OAHAND_TRM[track.BL$CCT_OAHAND_TRM==8]<- NA
track.BL$CCT_OAHAND_TRM[track.BL$CCT_OAHAND_TRM==9]<- NA
track.BL$CCT_OAHIP_TRM[track.BL$CCT_OAHIP_TRM==1]<- 1 #Hip arthritis
track.BL$CCT_OAHIP_TRM[track.BL$CCT_OAHIP_TRM==2]<- 0
track.BL$CCT_OAHIP_TRM[track.BL$CCT_OAHIP_TRM==8]<- NA
track.BL$CCT_OAHIP_TRM[track.BL$CCT_OAHIP_TRM==9]<- NA
track.BL$CCT_OAKNEE_TRM[track.BL$CCT_OAKNEE_TRM==1]<- 1 #Knee arthritis
track.BL$CCT_OAKNEE_TRM[track.BL$CCT_OAKNEE_TRM==2]<- 0
track.BL$CCT_OAKNEE_TRM[track.BL$CCT_OAKNEE_TRM==8]<- NA
track.BL$CCT_OAKNEE_TRM[track.BL$CCT_OAKNEE_TRM==9]<- NA
track.BL$CCT_RA_TRM[track.BL$CCT_RA_TRM==1]<- 1 #Rheumatoid arthritis
track.BL$CCT_RA_TRM[track.BL$CCT_RA_TRM==2]<- 0
track.BL$CCT_RA_TRM[track.BL$CCT_RA_TRM==8]<- NA
track.BL$CCT_RA_TRM[track.BL$CCT_RA_TRM==9]<- NA
track.BL$CCT_OTART_TRM[track.BL$CCT_OTART_TRM==1]<- 1 #Other arthritis
track.BL$CCT_OTART_TRM[track.BL$CCT_OTART_TRM==2]<- 0
track.BL$CCT_OTART_TRM[track.BL$CCT_OTART_TRM==8]<- NA
track.BL$CCT_OTART_TRM[track.BL$CCT_OTART_TRM==9]<- NA
track.BL$CCT_DIAB_TRM[track.BL$CCT_DIAB_TRM==1]<- 1 #Diabetes
track.BL$CCT_DIAB_TRM[track.BL$CCT_DIAB_TRM==2]<- 0
track.BL$CCT_DIAB_TRM[track.BL$CCT_DIAB_TRM==8]<- NA
track.BL$CCT_DIAB_TRM[track.BL$CCT_DIAB_TRM==9]<- NA
track.BL$CCT_HBP_TRM[track.BL$CCT_HBP_TRM==1]<- 1 #High blood pressure
track.BL$CCT_HBP_TRM[track.BL$CCT_HBP_TRM==2]<- 0
track.BL$CCC_HBP_COM[track.BL$CCT_HBP_TRM==8]<- NA
track.BL$CCT_HBP_TRM[track.BL$CCT_HBP_TRM==9]<- NA
track.BL$CCT_UTHYR_TRM[track.BL$CCT_UTHYR_TRM==1]<- 1 #Under active thyroid
track.BL$CCT_UTHYR_TRM[track.BL$CCT_UTHYR_TRM==2]<- 0
track.BL$CCT_UTHYR_TRM[track.BL$CCT_UTHYR_TRM==8]<- NA
track.BL$CCT_UTHYR_TRM[track.BL$CCT_UTHYR_TRM==9]<- NA
track.BL$CCT_ANGI_TRM[track.BL$CCT_ANGI_TRM==1]<- 1 #Angina
track.BL$CCT_ANGI_TRM[track.BL$CCT_ANGI_TRM==2]<- 0
track.BL$CCT_ANGI_TRM[track.BL$CCT_ANGI_TRM==8]<- NA
track.BL$CCT_ANGI_TRM[track.BL$CCT_ANGI_TRM==9]<- NA
track.BL$CCT_CVA_TRM[track.BL$CCT_CVA_TRM==1]<- 1 #Stroke or CVA
track.BL$CCT_CVA_TRM[track.BL$CCT_CVA_TRM==2]<- 0
track.BL$CCT_CVA_TRM[track.BL$CCT_CVA_TRM==8]<- NA
track.BL$CCT_CVA_TRM[track.BL$CCT_CVA_TRM==9]<- NA
track.BL$CCT_AMI_TRM[track.BL$CCT_AMI_TRM==1]<- 1 #myocardial infarction
track.BL$CCT_AMI_TRM[track.BL$CCT_AMI_TRM==2]<- 0
track.BL$CCT_AMI_TRM[track.BL$CCT_AMI_TRM==8]<- NA
track.BL$CCT_AMI_TRM[track.BL$CCT_AMI_TRM==9]<- NA
track.BL$CCT_OTHYR_TRM[track.BL$CCT_OTHYR_TRM==1]<- 1 #Overactive thyroid
track.BL$CCT_OTHYR_TRM[track.BL$CCT_OTHYR_TRM==2]<- 0
track.BL$CCT_OTHYR_TRM[track.BL$CCT_OTHYR_TRM==8]<- NA
track.BL$CCT_OTHYR_TRM[track.BL$CCT_OTHYR_TRM==9]<- NA
track.BL$CCT_TIA_TRM[track.BL$CCT_TIA_TRM==1]<- 1 #Transient Ischemic Attack
track.BL$CCT_TIA_TRM[track.BL$CCT_TIA_TRM==2]<- 0
track.BL$CCT_TIA_TRM[track.BL$CCT_TIA_TRM==8]<- NA
track.BL$CCT_TIA_TRM[track.BL$CCT_TIA_TRM==9]<- NA
track.BL$CCT_ASTHM_TRM[track.BL$CCT_ASTHM_TRM==1]<- 1 #Asthma
track.BL$CCT_ASTHM_TRM[track.BL$CCT_ASTHM_TRM==2]<- 0
track.BL$CCT_ASTHM_TRM[track.BL$CCT_ASTHM_TRM==8]<- NA
track.BL$CCT_ASTHM_TRM[track.BL$CCT_ASTHM_TRM==9]<- NA
track.BL$CCT_OSTPO_TRM[track.BL$CCT_OSTPO_TRM==1]<- 1 #Osteoperosis
track.BL$CCT_OSTPO_TRM[track.BL$CCT_OSTPO_TRM==2]<- 0
track.BL$CCT_OSTPO_TRM[track.BL$CCT_OSTPO_TRM==8]<- NA
track.BL$CCT_OSTPO_TRM[track.BL$CCT_OSTPO_TRM==9]<- NA
track.BL$CCT_PARK_TRM[track.BL$CCT_PARK_TRM==1]<- 1 #Parkinsons
track.BL$CCT_PARK_TRM[track.BL$CCT_PARK_TRM==2]<- 0
track.BL$CCT_PARK_TRM[track.BL$CCT_PARK_TRM==8]<- NA
track.BL$CCT_PARK_TRM[track.BL$CCT_PARK_TRM==9]<- NA
track.BL$CCT_COPD_TRM[track.BL$CCT_COPD_TRM==1]<- 1 #COPD
track.BL$CCT_COPD_TRM[track.BL$CCT_COPD_TRM==2]<- 0
track.BL$CCT_COPD_TRM[track.BL$CCT_COPD_TRM==8]<- NA
track.BL$CCT_COPD_TRM[track.BL$CCT_COPD_TRM==9]<- NA
track.BL$Chronic_conditions<-track.BL$CCT_HEART_TRM + track.BL$CCT_PVD_TRM + track.BL$CCT_MEMPB_TRM + track.BL$CCT_ALZH_TRM + track.BL$CCT_MS_TRM +
track.BL$CCT_EPIL_TRM + track.BL$CCT_MGRN_TRM + track.BL$CCT_ULCR_TRM +
track.BL$CCT_IBDIBS_TRM + track.BL$CCT_BOWINC_TRM + track.BL$CCT_URIINC_TRM + track.BL$CCT_MACDEG_TRM + track.BL$CCT_CANC_TRM + track.BL$CCT_BCKP_TRM + track.BL$CCT_KIDN_TRM +
track.BL$CCT_OTCCT_TRM + track.BL$CCT_OAHAND_TRM + track.BL$CCT_OAHIP_TRM + track.BL$CCT_OAKNEE_TRM + track.BL$CCT_RA_TRM + track.BL$CCT_OTART_TRM +
track.BL$CCT_DIAB_TRM + track.BL$CCT_HBP_TRM + track.BL$CCT_UTHYR_TRM + track.BL$CCT_ANGI_TRM + track.BL$CCT_CVA_TRM + track.BL$CCT_AMI_TRM + track.BL$CCT_OTHYR_TRM +
track.BL$CCT_TIA_TRM + track.BL$CCT_ASTHM_TRM + track.BL$CCT_OSTPO_TRM + track.BL$CCT_PARK_TRM + track.BL$CCT_COPD_TRM
#Restless Sleep (≥ 3-4 days/week)
track.BL$RSTLS_Sleep<-NA
track.BL$RSTLS_Sleep[track.BL$DEP_RSTLS_TRM<3]<-1
track.BL$RSTLS_Sleep[track.BL$DEP_RSTLS_TRM>=3 & track.BL$DEP_RSTLS_TRM<8]<-0
track.BL$RSTLS_Sleep[track.BL$DEP_RSTLS_TRM>4]<-NA
#Finalize data set
track.BL.1<-track.BL[c(1,3,4,72:80,101:109,82,111)]
names(track.BL.1) <-paste(names(track.BL.1),"_0", sep="")
track.BL.Final<- rename(track.BL.1, "ID" = "entity_id_0")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#Baseline (Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
track.BLcogs<-trackBL[c(1,1010,1011,1068,1055,1067,1065,1066,995,1058)]
############Cognitive Function##############
#~~~~~~Animal Fluency~~~~~~~~~~~#
track.BLcogs$Animal_Fluency_Lang<-NA
track.BLcogs$Animal_Fluency_Lang[track.BLcogs$COG_AFT_STARTLANG_TRM=="en"]<-"English"
track.BLcogs$Animal_Fluency_Lang[track.BLcogs$COG_AFT_STARTLANG_TRM=="fr"]<-"French"
track.BLcogs$Animal_Fluency_Strict<-track.BLcogs$COG_AFT_SCORE_1_TRM
track.BLcogs$Animal_Fluency_Lenient<-track.BLcogs$COG_AFT_SCORE_2_TRM
#~~~~~~~~Mental Alteration Test~~~~~~~~~~#
track.BLcogs$MAT_Lang<-NA
track.BLcogs$MAT_Lang[track.BLcogs$COG_MAT_STARTLANG_TRM=="en"]<-"English"
track.BLcogs$MAT_Lang[track.BLcogs$COG_MAT_STARTLANG_TRM=="fr"]<-"French"
track.BLcogs$MAT_Score<-track.BLcogs$COG_MAT_SCORE_TRM
#~~~~~~~~RVLT~~~~~~~~~~~~~~~~#
#Rey-Immediate Recall
track.BLcogs$RVLT_Immediate_Lang<- NA
track.BLcogs$RVLT_Immediate_Lang[track.BLcogs$COG_REYI_STARTLANG_TRM=="en"]<-"English"
track.BLcogs$RVLT_Immediate_Lang[track.BLcogs$COG_REYI_STARTLANG_TRM=="fr"]<-"French"
track.BLcogs$RVLT_Immediate_Score<-track.BLcogs$COG_REYI_SCORE_TRM
#Rey-Delayed Recall
track.BLcogs$RVLT_Delayed_Lang<- NA
track.BLcogs$RVLT_Delayed_Lang[track.BLcogs$COG_REYII_STARTLANG_TRM=="en"]<-"English"
track.BLcogs$RVLT_Delayed_Lang[track.BLcogs$COG_REYII_STARTLANG_TRM=="fr"]<-"French"
track.BLcogs$RVLT_Delayed_Score<-track.BLcogs$COG_REYII_SCORE_TRM
track.BLcogs1 <- track.BLcogs[c(1,11:19)]
names(track.BLcogs1) <-paste(names(track.BLcogs1),"_0", sep="")
track.BLcogs.Final<- rename(track.BLcogs1, "ID" = "entity_id_0")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#FU1 (Non-Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_FUP1")
trackFU1<-read.csv("/Users/ryan/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_FUP1/23SP001_McMaster_PRaina_FUP1_Trav3-1.csv")#Comprehensive Cohort Baseline
track.FU1<-trackFU1[c(1,75,76,594,83,1105,78,1859,134,155,174,181,187,204,201,210,223,228,240,
244,252,140,157,163,176,183,190,256,258,1832,1842,444,448,495,496,770,
423,434,451,575,455,458,461,462,463,470,473,514,523,537,399,396,392,401,
404,419,413,519,426,437,430,522,441,407,512,412,546,223,731)]
#Sex (no variable in FU1)
SexFU1 <- track.FU1 %>%
left_join(track.BL, by = "entity_id") %>%
select(entity_id, Sex)
track.FU1$Sex<-SexFU1$Sex
#Age
track.FU1$Age<-track.FU1$AGE_NMBR_TRF1
#Marital Status
track.FU1$Relationship_status<-NA
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==1]<-"Single"
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==2]<-"Married"
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==3]<-"Widowed"
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==4]<-"Divorced"
track.FU1$Relationship_status[track.FU1$SDC_MRTL_TRF1==5]<-"Separated"
#Education 4 Category (No variable in FU1)
EducationFU1 <- track.FU1 %>%
left_join(track.BL, by = "entity_id") %>%
select(entity_id,Education4)
track.FU1$Education4<-EducationFU1$Education4
#Household Income
track.FU1$Income_Level<-NA
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==1]<-"<$20k"
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==2]<-"$20-50k"
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==3]<-"$50-100k"
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==4]<-"$100-150k"
track.FU1$Income_Level[track.FU1$INC_PTOT_TRF1==5]<-">$150k"
#Living Status
track.FU1$Living_status<-NA
track.FU1$Living_status[track.FU1$OWN_DWLG_TRF1==1]<-"House"
track.FU1$Living_status[track.FU1$OWN_DWLG_TRF1==2 |track.FU1$OWN_DWLG_TRF1==6]<-"Apartment/Condo/Townhome"
track.FU1$Living_status[track.FU1$OWN_DWLG_TRF1==3]<-"Assisted Living"
track.FU1$Living_status[track.FU1$OWN_DWLG_TRF1==4 | track.FU1$OWN_DWLG_TRF1==5 | track.FU1$OWN_DWLG_TRF1>=7]<-"Other"
#Alcohol
track.FU1$Alcohol<-NA
track.FU1$Alcohol[track.FU1$ALC_TTM_TRF1==1]<-"Regular drinker (at least once a month)"
track.FU1$Alcohol[track.FU1$ALC_TTM_TRF1==2]<-"Occasional drinker"
track.FU1$Alcohol[track.FU1$ALC_TTM_TRF1==3]<-"Non-drinker"
#Smoking Status (no variable in FU1)
SmokingFU1 <- track.FU1 %>%
left_join(track.BL, by = "entity_id") %>%
select(entity_id,Smoking_Status)
track.FU1$Smoking_Status<-SmokingFU1$Smoking_Status
#Ethnicity (take from baseline)
EthnicityFU1 <- track.FU1 %>%
left_join(track.BL, by = "entity_id") %>%
select(entity_id, Ethnicity)
track.FU1$Ethnicity<-EthnicityFU1$Ethnicity
#############Physical Activity Scale for the Elderly######################
#Q1: Sitting Activity Frequency in Past 7 days
track.FU1$PASE_Q1<-NA
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1==1]<-0
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1==2]<-0.11
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1==3]<-0.25
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1==4]<-0.43
track.FU1$PASE_Q1[track.FU1$PA2_SIT_TRF1>4]<-NA
track.FU1$PASE_Q1[is.na(track.FU1$PA2_SIT_TRF1)]<-NA
track.FU1$PASE_Q1B<-NA
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==1]<-0
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==2]<-1
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==3]<-3
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==4]<-6
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1==5]<-10
track.FU1$PASE_Q1B[track.FU1$PA2_SITHR_TRF1>5]<- 0
track.FU1$PASE_Q1B[is.na(track.FU1$PA2_SITHR_TRF1)]<-NA
track.FU1$PASE_Q1B<-as.numeric(track.FU1$PASE_Q1B)
#Q2: Walking outside frequency
track.FU1$PASE_Q2<- NULL
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1==1]<-0
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1==2]<-0.11
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1==3]<-0.25
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1==4]<-0.43
track.FU1$PASE_Q2[track.FU1$PA2_WALK_TRF1>4]<-NA
track.FU1$PASE_Q2[is.na(track.FU1$PA2_WALK_TRF1)]<-NA
track.FU1$PASE_Q2A<- NA
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==1]<-0
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==2]<-1
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==3]<-3
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==4]<-6
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1==5]<-10
track.FU1$PASE_Q2A[track.FU1$PA2_WALKHR_TRF1>5]<-NA
track.FU1$PASE_Q2A[is.na(track.FU1$PA2_WALKHR_TRF1)]<-NA
track.FU1$PASE_Q2A<-as.numeric(track.FU1$PASE_Q2A)
#Q3: Light Sports or Activity Frequency
track.FU1$PASE_Q3<- NA
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1==1]<-0
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1==2]<-0.11
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1==3]<-0.25
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1==4]<-0.43
track.FU1$PASE_Q3[track.FU1$PA2_LSPRT_TRF1>4]<-NA
track.FU1$PASE_Q3[is.na(track.FU1$PA2_LSPRT_TRF1)]<-NA
track.FU1$PASE_Q3A<- NA
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==1]<-0
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==2]<-1
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==3]<-3
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==4]<-6
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1==5]<-10
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1>5]<-NA
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1 == 1 | is.na(track.FU1$PA2_LSPRTHR_TRF1)]<-0
track.FU1$PASE_Q3A[is.na(track.FU1$PA2_LSPRT_TRF1) & is.na(track.FU1$PA2_LSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q3A[track.FU1$PA2_LSPRTHR_TRF1>4 & is.na(track.FU1$PA2_LSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q3A<-as.numeric(track.FU1$PASE_Q3A)
#Q4: Moderate Sports or Activity Frequency
track.FU1$PASE_Q4<-NA
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1==1]<-0
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1==2]<-0.11
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1==3]<-0.25
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1==4]<-0.43
track.FU1$PASE_Q4[track.FU1$PA2_MSPRT_TRF1>4]<-NA
track.FU1$PASE_Q4[is.na(track.FU1$PA2_MSPRT_TRF1)]<-NA
track.FU1$PASE_Q4A<- NA
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==1]<-0
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==2]<-1
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==3]<-3
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==4]<-6
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1==5]<-10
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRTHR_TRF1>5]<-NA
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRT_TRF1 == 1 | is.na(track.FU1$PA2_MSPRTHR_TRF1)]<-0
track.FU1$PASE_Q4A[is.na(track.FU1$PA2_MSPRT_TRF1) & is.na(track.FU1$PA2_MSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q4A[track.FU1$PA2_MSPRT_TRF1>4 & is.na(track.FU1$PA2_MSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q4A<-as.numeric(track.FU1$PASE_Q4A)
#Q5: Strenuous Sports or Activity Frequency
track.FU1$PASE_Q5<-NA
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1==1]<-0
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1==2]<-0.11
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1==3]<-0.25
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1==4]<-0.43
track.FU1$PASE_Q5[track.FU1$PA2_SSPRT_TRF1>4]<-NA
track.FU1$PASE_Q5[is.na(track.FU1$PA2_SSPRT_TRF1)]<-NA
track.FU1$PASE_Q5A<- NA
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==1]<-0
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==2]<-1
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==3]<-3
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==4]<-6
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1==5]<-10
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRTHR_TRF1>5]<-NA
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRT_TRF1 == 1 | is.na(track.FU1$PA2_SSPRTHR_TRF1)]<-0
track.FU1$PASE_Q5A[is.na(track.FU1$PA2_SSPRT_TRF1) & is.na(track.FU1$PA2_SSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q5A[track.FU1$PA2_SSPRT_TRF1>4 & is.na(track.FU1$PA2_SSPRTHR_TRF1)]<-NA
track.FU1$PASE_Q5A<-as.numeric(track.FU1$PASE_Q5A)
#Q6: Muscle strengthening and endurance exercise
track.FU1$PASE_Q6<-NA
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1==1]<-0
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1==2]<-0.11
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1==3]<-0.25
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1==4]<-0.43
track.FU1$PASE_Q6[track.FU1$PA2_EXER_TRF1>4]<-NA
track.FU1$PASE_Q6[is.na(track.FU1$PA2_EXER_TRF1)]<-NA
track.FU1$PASE_Q6A<- NA
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==1]<-0
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==2]<-1
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==3]<-3
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==4]<-6
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1==5]<-10
track.FU1$PASE_Q6A[track.FU1$PA2_EXERHR_TRF1>5]<-NA
track.FU1$PASE_Q6A[track.FU1$PA2_EXER_TRF1 == 1 | is.na(track.FU1$PA2_EXERHR_TRF1)]<-0
track.FU1$PASE_Q6A[is.na(track.FU1$PA2_EXER_TRF1) & is.na(track.FU1$PA2_EXERHR_TRF1)]<-NA
track.FU1$PASE_Q6A[track.FU1$PA2_EXER_TRF1>4 & is.na(track.FU1$PA2_EXERHR_TRF1)]<-NA
track.FU1$PASE_Q6A<-as.numeric(track.FU1$PASE_Q6A)
#Q7: Light Housework
track.FU1$PASE_Q7<-NA
track.FU1$PASE_Q7[track.FU1$PA2_LTHSWK_TRF1==1]<-1
track.FU1$PASE_Q7[track.FU1$PA2_LTHSWK_TRF1==2]<-0
track.FU1$PASE_Q7[track.FU1$PA2_LTHSWK_TRF1>2]<-NA
track.FU1$PASE_Q7[is.na(track.FU1$PA2_LTHSWK_TRF1)]<-NA
track.FU1$PASE_Q7<-as.numeric(track.FU1$PASE_Q7)
#Q8: Heavy Housework
track.FU1$PASE_Q8<-NA
track.FU1$PASE_Q8[track.FU1$PA2_HVYHSWK_TRF1==1]<-1
track.FU1$PASE_Q8[track.FU1$PA2_HVYHSWK_TRF1==2]<-0
track.FU1$PASE_Q8[track.FU1$PA2_HVYHSWK_TRF1>2]<-NA
track.FU1$PASE_Q8[is.na(track.FU1$PA2_HVYHSWK_TRF1)]<-NA
track.FU1$PASE_Q8<-as.numeric(track.FU1$PASE_Q8)
#Q9: Home Repair, Yardwork, Gardening, Care for another person
track.FU1$PASE_Q9A<-NA
track.FU1$PASE_Q9A[track.FU1$PA2_HMREPAIR_TRF1==1]<-1
track.FU1$PASE_Q9A[track.FU1$PA2_HMREPAIR_TRF1==2]<-0
track.FU1$PASE_Q9A[track.FU1$PA2_HMREPAIR_TRF1>2]<-NA
track.FU1$PASE_Q9A[is.na(track.FU1$PA2_HMREPAIR_TRF1)]<-NA
track.FU1$PASE_Q9A<-as.numeric(track.FU1$PASE_Q9A)
track.FU1$PASE_Q9B<-NA
track.FU1$PASE_Q9B[track.FU1$PA2_HVYODA_TRF1==1]<-1
track.FU1$PASE_Q9B[track.FU1$PA2_HVYODA_TRF1==2]<-0
track.FU1$PASE_Q9B[track.FU1$PA2_HVYODA_TRF1>2]<-NA
track.FU1$PASE_Q9B[is.na(track.FU1$PA2_HVYODA_TRF1)]<-NA
track.FU1$PASE_Q9B<-as.numeric(track.FU1$PASE_Q9B)
track.FU1$PASE_Q9C<-NA
track.FU1$PASE_Q9C[track.FU1$PA2_LTODA_TRF1==1]<-1
track.FU1$PASE_Q9C[track.FU1$PA2_LTODA_TRF1==2]<-0
track.FU1$PASE_Q9C[track.FU1$PA2_LTODA_TRF1>2]<-NA
track.FU1$PASE_Q9C[is.na(track.FU1$PA2_LTODA_TRF1)]<-NA
track.FU1$PASE_Q9C<-as.numeric(track.FU1$PASE_Q9C)
track.FU1$PASE_Q9D<-NA
track.FU1$PASE_Q9D[track.FU1$PA2_CRPRSN_TRF1==1]<-1
track.FU1$PASE_Q9D[track.FU1$PA2_CRPRSN_TRF1==2]<-0
track.FU1$PASE_Q9D[track.FU1$PA2_CRPRSN_TRF1>2]<-NA
track.FU1$PASE_Q9D[is.na(track.FU1$PA2_CRPRSN_TRF1)]<-NA
track.FU1$PASE_Q9D<-as.numeric(track.FU1$PASE_Q9D)
#Q10: Working and Volunteering
track.FU1$PASE_Q10<-NA
track.FU1$PASE_Q10[track.FU1$PA2_WRK_TRF1==1]<-1
track.FU1$PASE_Q10[track.FU1$PA2_WRK_TRF1==2]<-0
track.FU1$PASE_Q10[track.FU1$PA2_WRK_TRF1>2]<-NA
track.FU1$PASE_Q10[is.na(track.FU1$PA2_WRK_TRF1)]<-NA
track.FU1$PASE_Q10<-as.numeric(track.FU1$PASE_Q10)
track.FU1$PASE_Q10A<-NA
track.FU1$PASE_Q10A<-track.FU1$PA2_WRKHRS_NB_TRF1
track.FU1$PASE_Q10A[track.FU1$PA2_WRKHRS_NB_TRF1>=700]<-NA
track.FU1$PASE_Q10A[track.FU1$PA2_WRK_TRF1 == 2 | is.na(track.FU1$PA2_WRKHRS_NB_TRF1)]<-0
track.FU1$PASE_Q10A[is.na(track.FU1$PA2_WRK_TRF1) & is.na(track.FU1$PA2_WRKHRS_NB_TRF1)]<-NA
track.FU1$PASE_Q10A<-as.numeric(track.FU1$PASE_Q10A)
track.FU1$PASE_Q10A<-track.FU1$PASE_Q10A/7
#PASE TOTAL SCORE#
track.FU1$PASE_TOTAL<-track.FU1$PASE_Q2*track.FU1$PASE_Q2A*20 + track.FU1$PASE_Q3*track.FU1$PASE_Q3A*21 + track.FU1$PASE_Q4*track.FU1$PASE_Q4A*23 + track.FU1$PASE_Q5*track.FU1$PASE_Q5A*30 +
track.FU1$PASE_Q6*track.FU1$PASE_Q6A*30 + (track.FU1$PASE_Q7+track.FU1$PASE_Q8)*25 + track.FU1$PASE_Q9A*30 + track.FU1$PASE_Q9B*36 + track.FU1$PASE_Q9C*20 + track.FU1$PASE_Q9D*35 +
track.FU1$PASE_Q10*track.FU1$PASE_Q10A*21
#BMI
track.FU1$BMI<-track.FU1$HWT_DBMI_TRF1
track.FU1$BMI[track.FU1$HWT_DBMI_TRF1>100]<-NA
#CESD-10
track.FU1$CESD_10<-track.FU1$DEP_CESD10_TRF1
track.FU1$CESD_10[track.FU1$DEP_CESD10_TRF1==98]<-NA
track.FU1$CESD_10[track.FU1$DEP_CESD10_TRF1<0]<-NA
#Subjective Cognitive Impairment
track.FU1$SCI<- NA
track.FU1$SCI[track.FU1$CCT_MEMPB_TRF1==1]<- "Yes"
track.FU1$SCI[track.FU1$CCT_MEMPB_TRF1==2]<- "No"
#Dementia and AD
track.FU1$Dementia<- NA
track.FU1$Dementia[track.FU1$CCT_ALZH_TRF1==1]<- "Yes"
track.FU1$Dementia[track.FU1$CCT_ALZH_TRF1==2]<- "No"
#Anxiety
track.FU1$Anxiety<- NA
track.FU1$Anxiety[track.FU1$CCT_ANXI_TRF1==1]<- "Yes"
track.FU1$Anxiety[track.FU1$CCT_ANXI_TRF1==2]<- "No"
#Mood Disorders
track.FU1$Mood_Disord<- NA
track.FU1$Mood_Disord[track.FU1$CCT_MOOD_TRF1==1]<- "Yes"
track.FU1$Mood_Disord[track.FU1$CCT_MOOD_TRF1==2]<- "No"
#Pet Ownership at Baseline
track.FU1$Pet_Owner<-NA
track.FU1$Pet_Owner[track.FU1$SSA_PET_TRF1==1]<-"Yes"
track.FU1$Pet_Owner[track.FU1$SSA_PET_TRF1==2]<-"No"
#Number of Chronic Conditions
track.FU1$Chronic_conditions<-NA
track.FU1$CCT_HEART_TRM[track.FU1$CCT_HEART_TRF1==1]<- 1 #Heart Disease
track.FU1$CCT_HEART_TRM[track.FU1$CCT_HEART_TRF1==2]<- 0
track.FU1$CCT_HEART_TRM[track.FU1$CCT_HEART_TRF1==8]<- NA
track.FU1$CCT_HEART_TRM[track.FU1$CCT_HEART_TRF1==9]<- NA
track.FU1$CCT_PVD_TRM[track.FU1$CCT_PVD_TRF1==1]<- 1 #peripheral vascular disease
track.FU1$CCT_PVD_TRM[track.FU1$CCT_PVD_TRF1==2]<- 0
track.FU1$CCT_PVD_TRM[track.FU1$CCT_PVD_TRF1==8]<- NA
track.FU1$CCT_PVD_TRM[track.FU1$CCT_PVD_TRF1==9]<- NA
track.FU1$CCT_MEMPB_TRM[track.FU1$CCT_MEMPB_TRF1==1]<- 1 #SCI
track.FU1$CCT_MEMPB_TRM[track.FU1$CCT_MEMPB_TRF1==2]<- 0
track.FU1$CCT_MEMPB_TRM[track.FU1$CCT_MEMPB_TRF1==8]<- NA
track.FU1$CCT_MEMPB_TRM[track.FU1$CCT_MEMPB_TRF1==9]<- NA
track.FU1$CCT_ALZH_TRM[track.FU1$CCT_ALZH_TRF1==1]<- 1 #Alzheimers or demeinta
track.FU1$CCT_ALZH_TRM[track.FU1$CCT_ALZH_TRF1==2]<- 0
track.FU1$CCT_ALZH_TRM[track.FU1$CCT_ALZH_TRF1==8]<- NA
track.FU1$CCT_ALZH_TRM[track.FU1$CCT_ALZH_TRF1==9]<- NA
track.FU1$CCT_MS_TRM[track.FU1$CCT_MS_TRF1==1]<- 1 #Multiple sclerosis
track.FU1$CCT_MS_TRM[track.FU1$CCT_MS_TRF1==2]<- 0
track.FU1$CCT_MS_TRM[track.FU1$CCT_MS_TRF1==8]<- NA
track.FU1$CCT_MS_TRM[track.FU1$CCT_MS_TRF1==9]<- NA
track.FU1$CCT_EPIL_TRM[track.FU1$EPI_EVER_TRF1==1]<- 1 #Epilepsy
track.FU1$CCT_EPIL_TRM[track.FU1$EPI_EVER_TRF1==2]<- 0
track.FU1$CCT_EPIL_TRM[track.FU1$EPI_EVER_TRF1==8]<- NA
track.FU1$CCT_EPIL_TRM[track.FU1$EPI_EVER_TRF1==9]<- NA
track.FU1$CCT_MGRN_TRM[track.FU1$CCT_MGRN_TRF1==1]<- 1 #Migraine headaches
track.FU1$CCT_MGRN_TRM[track.FU1$CCT_MGRN_TRF1==2]<- 0
track.FU1$CCT_MGRN_TRM[track.FU1$CCT_MGRN_TRF1==8]<- NA
track.FU1$CCT_MGRN_TRM[track.FU1$CCT_MGRN_TRF1==9]<- NA
track.FU1$CCT_ULCR_TRM[track.FU1$CCT_ULCR_TRF1==1]<- 1 #Intenstinal or stomach ulcers
track.FU1$CCT_ULCR_TRM[track.FU1$CCT_ULCR_TRF1==2]<- 0
track.FU1$CCT_ULCR_TRM[track.FU1$CCT_ULCR_TRF1==8]<- NA
track.FU1$CCT_ULCR_TRM[track.FU1$CCT_ULCR_TRF1==9]<- NA
track.FU1$CCT_IBDIBS_TRM[track.FU1$CCT_IBDIBS_TRF1==1]<- 1 #Bowel disorder
track.FU1$CCT_IBDIBS_TRM[track.FU1$CCT_IBDIBS_TRF1==2]<- 0
track.FU1$CCT_IBDIBS_TRM[track.FU1$CCT_IBDIBS_TRF1==8]<- NA
track.FU1$CCT_IBDIBS_TRM[track.FU1$CCT_IBDIBS_TRF1==9]<- NA
track.FU1$CCT_BOWINC_TRM[track.FU1$CCT_BOWINC_TRF1==1]<- 1 #Bowel incontinence
track.FU1$CCT_BOWINC_TRM[track.FU1$CCT_BOWINC_TRF1==2]<- 0
track.FU1$CCT_BOWINC_TRM[track.FU1$CCT_BOWINC_TRF1==8]<- NA
track.FU1$CCT_BOWINC_TRM[track.FU1$CCT_BOWINC_TRF1==9]<- NA
track.FU1$CCT_URIINC_TRM[track.FU1$CCT_URIINC_TRF1==1]<- 1 #Urinary incontinence
track.FU1$CCT_URIINC_TRM[track.FU1$CCT_URIINC_TRF1==2]<- 0
track.FU1$CCT_URIINC_TRM[track.FU1$CCT_URIINC_TRF1==8]<- NA
track.FU1$CCT_URIINC_TRM[track.FU1$CCT_URIINC_TRF1==9]<- NA
track.FU1$CCT_MACDEG_TRM[track.FU1$CCT_MACDEG_TRF1==1]<- 1 #Macular degeneration
track.FU1$CCT_MACDEG_TRM[track.FU1$CCT_MACDEG_TRF1==2]<- 0
track.FU1$CCT_MACDEG_TRM[track.FU1$CCT_MACDEG_TRF1==8]<- NA
track.FU1$CCT_MACDEG_TRM[track.FU1$CCT_MACDEG_TRF1==9]<- NA
track.FU1$CCT_CANC_TRM[track.FU1$CCT_CANC_TRF1==1]<- 1 #All-cause cancer
track.FU1$CCT_CANC_TRM[track.FU1$CCT_CANC_TRF1==2]<- 0
track.FU1$CCT_CANC_TRM[track.FU1$CCT_CANC_TRF1==8]<- NA
track.FU1$CCT_CANC_TRM[track.FU1$CCT_CANC_TRF1==9]<- NA
track.FU1$CCT_BCKP_TRM[track.FU1$CCT_BCKP_TRF1==1]<- 1 #Back problems but not fibromyalgia or arthritis
track.FU1$CCT_BCKP_TRM[track.FU1$CCT_BCKP_TRF1==2]<- 0
track.FU1$CCT_BCKP_TRM[track.FU1$CCT_BCKP_TRF1==8]<- NA
track.FU1$CCT_BCKP_TRM[track.FU1$CCT_BCKP_TRF1==9]<- NA
track.FU1$CCT_KIDN_TRM[track.FU1$CCT_KIDN_TRF1==1]<- 1 #Kidney disease
track.FU1$CCT_KIDN_TRM[track.FU1$CCT_KIDN_TRF1==2]<- 0
track.FU1$CCT_KIDN_TRM[track.FU1$CCT_KIDN_TRF1==8]<- NA
track.FU1$CCT_KIDN_TRM[track.FU1$CCT_KIDN_TRF1==9]<- NA
track.FU1$CCT_OTCCT_TRM[track.FU1$CCT_OTCCT_TRF1==1]<- 1 #Other long term mental or physical condition
track.FU1$CCT_OTCCT_TRM[track.FU1$CCT_OTCCT_TRF1==2]<- 0
track.FU1$CCT_OTCCT_TRM[track.FU1$CCT_OTCCT_TRF1==8]<- NA
track.FU1$CCT_OTCCT_TRM[track.FU1$CCT_OTCCT_TRF1==9]<- NA
track.FU1$CCT_OAHAND_TRM[track.FU1$CCT_OAHAND_TRF1==1]<- 1 #Hand arthritis
track.FU1$CCT_OAHAND_TRM[track.FU1$CCT_OAHAND_TRF1==2]<- 0
track.FU1$CCT_OAHAND_TRM[track.FU1$CCT_OAHAND_TRF1==8]<- NA
track.FU1$CCT_OAHAND_TRM[track.FU1$CCT_OAHAND_TRF1==9]<- NA
track.FU1$CCT_OAHIP_TRM[track.FU1$CCT_OAHIP_TRF1==1]<- 1 #Hip arthritis
track.FU1$CCT_OAHIP_TRM[track.FU1$CCT_OAHIP_TRF1==2]<- 0
track.FU1$CCT_OAHIP_TRM[track.FU1$CCT_OAHIP_TRF1==8]<- NA
track.FU1$CCT_OAHIP_TRM[track.FU1$CCT_OAHIP_TRF1==9]<- NA
track.FU1$CCT_OAKNEE_TRM[track.FU1$CCT_OAKNEE_TRF1==1]<- 1 #Knee arthritis
track.FU1$CCT_OAKNEE_TRM[track.FU1$CCT_OAKNEE_TRF1==2]<- 0
track.FU1$CCT_OAKNEE_TRM[track.FU1$CCT_OAKNEE_TRF1==8]<- NA
track.FU1$CCT_OAKNEE_TRM[track.FU1$CCT_OAKNEE_TRF1==9]<- NA
track.FU1$CCT_RA_TRM[track.FU1$CCT_RA_TRF1==1]<- 1 #Rheumatoid arthritis
track.FU1$CCT_RA_TRM[track.FU1$CCT_RA_TRF1==2]<- 0
track.FU1$CCT_RA_TRM[track.FU1$CCT_RA_TRF1==8]<- NA
track.FU1$CCT_RA_TRM[track.FU1$CCT_RA_TRF1==9]<- NA
track.FU1$CCT_OTART_TRM[track.FU1$CCT_OTART_TRF1==1]<- 1 #Other arthritis
track.FU1$CCT_OTART_TRM[track.FU1$CCT_OTART_TRF1==2]<- 0
track.FU1$CCT_OTART_TRM[track.FU1$CCT_OTART_TRF1==8]<- NA
track.FU1$CCT_OTART_TRM[track.FU1$CCT_OTART_TRF1==9]<- NA
track.FU1$CCT_DIAB_TRM[track.FU1$CCT_DIAB_TRF1==1]<- 1 #Diabetes
track.FU1$CCT_DIAB_TRM[track.FU1$CCT_DIAB_TRF1==2]<- 0
track.FU1$CCT_DIAB_TRM[track.FU1$CCT_DIAB_TRF1==8]<- NA
track.FU1$CCT_DIAB_TRM[track.FU1$CCT_DIAB_TRF1==9]<- NA
track.FU1$CCT_HBP_TRM[track.FU1$CCT_HBP_TRF1==1]<- 1 #High blood pressure
track.FU1$CCT_HBP_TRM[track.FU1$CCT_HBP_TRF1==2]<- 0
track.FU1$CCT_HBP_TRM[track.FU1$CCT_HBP_TRF1==8]<- NA
track.FU1$CCT_HBP_TRM[track.FU1$CCT_HBP_TRF1==9]<- NA
track.FU1$CCT_UTHYR_TRM[track.FU1$CCT_UTHYR_TRF1==1]<- 1 #Under active thyroid
track.FU1$CCT_UTHYR_TRM[track.FU1$CCT_UTHYR_TRF1==2]<- 0
track.FU1$CCT_UTHYR_TRM[track.FU1$CCT_UTHYR_TRF1==8]<- NA
track.FU1$CCT_UTHYR_TRM[track.FU1$CCT_UTHYR_TRF1==9]<- NA
track.FU1$CCT_ANGI_TRM[track.FU1$CCT_ANGI_TRF1==1]<- 1 #Angina
track.FU1$CCT_ANGI_TRM[track.FU1$CCT_ANGI_TRF1==2]<- 0
track.FU1$CCT_ANGI_TRM[track.FU1$CCT_ANGI_TRF1==8]<- NA
track.FU1$CCT_ANGI_TRM[track.FU1$CCT_ANGI_TRF1==9]<- NA
track.FU1$CCT_CVA_TRM[track.FU1$CCT_CVA_TRF1==1]<- 1 #Stroke or CVA
track.FU1$CCT_CVA_TRM[track.FU1$CCT_CVA_TRF1==2]<- 0
track.FU1$CCT_CVA_TRM[track.FU1$CCT_CVA_TRF1==8]<- NA
track.FU1$CCT_CVA_TRM[track.FU1$CCT_CVA_TRF1==9]<- NA
track.FU1$CCT_AMI_TRM[track.FU1$CCT_AMI_TRF1==1]<- 1 #myocardial infarction
track.FU1$CCT_AMI_TRM[track.FU1$CCT_AMI_TRF1==2]<- 0
track.FU1$CCT_AMI_TRM[track.FU1$CCT_AMI_TRF1==8]<- NA
track.FU1$CCT_AMI_TRM[track.FU1$CCT_AMI_TRF1==9]<- NA
track.FU1$CCT_OTHYR_TRM[track.FU1$CCT_OTHYR_TRF1==1]<- 1 #Overactive thyroid
track.FU1$CCT_OTHYR_TRM[track.FU1$CCT_OTHYR_TRF1==2]<- 0
track.FU1$CCT_OTHYR_TRM[track.FU1$CCT_OTHYR_TRF1==8]<- NA
track.FU1$CCT_OTHYR_TRM[track.FU1$CCT_OTHYR_TRF1==9]<- NA
track.FU1$CCT_TIA_TRM[track.FU1$CCT_TIA_TRF1==1]<- 1 #Transient Ischemic Attack
track.FU1$CCT_TIA_TRM[track.FU1$CCT_TIA_TRF1==2]<- 0
track.FU1$CCT_TIA_TRM[track.FU1$CCT_TIA_TRF1==8]<- NA
track.FU1$CCT_TIA_TRM[track.FU1$CCT_TIA_TRF1==9]<- NA
track.FU1$CCT_ASTHM_TRM[track.FU1$CCT_ASTHM_TRF1==1]<- 1 #Asthma
track.FU1$CCT_ASTHM_TRM[track.FU1$CCT_ASTHM_TRF1==2]<- 0
track.FU1$CCT_ASTHM_TRM[track.FU1$CCT_ASTHM_TRF1==8]<- NA
track.FU1$CCT_ASTHM_TRM[track.FU1$CCT_ASTHM_TRF1==9]<- NA
track.FU1$CCT_OSTPO_TRM[track.FU1$CCT_OSTPO_TRF1==1]<- 1 #Osteoperosis
track.FU1$CCT_OSTPO_TRM[track.FU1$CCT_OSTPO_TRF1==2]<- 0
track.FU1$CCT_OSTPO_TRM[track.FU1$CCT_OSTPO_TRF1==8]<- NA
track.FU1$CCT_OSTPO_TRM[track.FU1$CCT_OSTPO_TRF1==9]<- NA
track.FU1$CCT_PARK_TRM[track.FU1$PKD_PARK_TRF1==1]<- 1 #Parkinsons
track.FU1$CCT_PARK_TRM[track.FU1$PKD_PARK_TRF1==2]<- 0
track.FU1$CCT_PARK_TRM[track.FU1$PKD_PARK_TRF1==8]<- NA
track.FU1$CCT_PARK_TRM[track.FU1$PKD_PARK_TRF1==9]<- NA
track.FU1$CCT_COPD_TRM[track.FU1$CCT_COPD_TRF1==1]<- 1 #COPD
track.FU1$CCT_COPD_TRM[track.FU1$CCT_COPD_TRF1==2]<- 0
track.FU1$CCT_COPD_TRM[track.FU1$CCT_COPD_TRF1==8]<- NA
track.FU1$CCT_COPD_TRM[track.FU1$CCT_COPD_TRF1==9]<- NA
track.FU1$Chronic_conditions<-track.FU1$CCT_HEART_TRM + track.FU1$CCT_PVD_TRM + track.FU1$CCT_MEMPB_TRM + track.FU1$CCT_ALZH_TRM + track.FU1$CCT_MS_TRM +
track.FU1$CCT_EPIL_TRM + track.FU1$CCT_MGRN_TRM + track.FU1$CCT_ULCR_TRM +
track.FU1$CCT_IBDIBS_TRM + track.FU1$CCT_BOWINC_TRM + track.FU1$CCT_URIINC_TRM + track.FU1$CCT_MACDEG_TRM + track.FU1$CCT_CANC_TRM + track.FU1$CCT_BCKP_TRM + track.FU1$CCT_KIDN_TRM +
track.FU1$CCT_OTCCT_TRM + track.FU1$CCT_OAHAND_TRM + track.FU1$CCT_OAHIP_TRM + track.FU1$CCT_OAKNEE_TRM + track.FU1$CCT_RA_TRM + track.FU1$CCT_OTART_TRM +
track.FU1$CCT_DIAB_TRM + track.FU1$CCT_HBP_TRM + track.FU1$CCT_UTHYR_TRM + track.FU1$CCT_ANGI_TRM + track.FU1$CCT_CVA_TRM + track.FU1$CCT_AMI_TRM + track.FU1$CCT_OTHYR_TRM +
track.FU1$CCT_TIA_TRM + track.FU1$CCT_ASTHM_TRM + track.FU1$CCT_OSTPO_TRM + track.FU1$CCT_PARK_TRM + track.FU1$CCT_COPD_TRM
#Restless Sleep (≥ 3-4 days/week)
track.FU1$RSTLS_Sleep<-NA
track.FU1$RSTLS_Sleep[track.FU1$DEP_RSTLS_TRF1<3]<-1
track.FU1$RSTLS_Sleep[track.FU1$DEP_RSTLS_TRF1>=3 & track.FU1$DEP_RSTLS_TRF1<8]<-0
track.FU1$RSTLS_Sleep[track.FU1$DEP_RSTLS_TRF1>4]<-NA
#Finalize data set
track.FU1.1<-track.FU1[c(1:3,70:78,99:107,80,141)]
names(track.FU1.1) <-paste(names(track.FU1.1),"_1", sep="")
track.FU1.Final<- rename(track.FU1.1, "ID" = "entity_id_1")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#FU1 (Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
track.FU1cogs<-trackFU1[c(1,1927,1928,1876,1875,1966,1877,1914,1878,1969)]
############Cognitive Function##############
#~~~~~~Animal Fluency~~~~~~~~~~~#
track.FU1cogs$Animal_Fluency_Lang<-NA
track.FU1cogs$Animal_Fluency_Lang[track.FU1cogs$COG_AFT_STARTLANG_TRF1=="en"]<-"English"
track.FU1cogs$Animal_Fluency_Lang[track.FU1cogs$COG_AFT_STARTLANG_TRF1=="fr"]<-"French"
track.FU1cogs$Animal_Fluency_Strict<-track.FU1cogs$COG_AFT_SCORE_1_TRF1
track.FU1cogs$Animal_Fluency_Strict[track.FU1cogs$Animal_Fluency_Strict<0]<-NA
track.FU1cogs$Animal_Fluency_Lenient<-track.FU1cogs$COG_AFT_SCORE_2_TRF1
track.FU1cogs$Animal_Fluency_Lenient[track.FU1cogs$Animal_Fluency_Lenient<0]<-NA
#~~~~~~~~Mental Alteration Test~~~~~~~~~~#
track.FU1cogs$MAT_Lang<-NA
track.FU1cogs$MAT_Lang[track.FU1cogs$COG_MAT_STARTLANG_TRF1=="en"]<-"English"
track.FU1cogs$MAT_Lang[track.FU1cogs$COG_MAT_STARTLANG_TRF1=="fr"]<-"French"
track.FU1cogs$MAT_Score<-track.FU1cogs$COG_MAT_SCORE_TRF1
track.FU1cogs$MAT_Score[track.FU1cogs$MAT_Score<0]<-NA
#~~~~~~~~RVLT~~~~~~~~~~~~~~~~#
#Rey-Immediate Recall
track.FU1cogs$RVLT_Immediate_Lang<- NA
track.FU1cogs$RVLT_Immediate_Lang[track.FU1cogs$COG_REYI_LANG_TRF1=="en"]<-"English"
track.FU1cogs$RVLT_Immediate_Lang[track.FU1cogs$COG_REYI_LANG_TRF1=="fr"]<-"French"
track.FU1cogs$RVLT_Immediate_Score<-track.FU1cogs$COG_REYI_SCORE_TRF1
track.FU1cogs$RVLT_Immediate_Score[track.FU1cogs$RVLT_Immediate_Score<0]<-NA
#Rey-Delayed Recall
track.FU1cogs$RVLT_Delayed_Lang<- NA
track.FU1cogs$RVLT_Delayed_Lang[track.FU1cogs$COG_REYII_LANG_TRF1=="en"]<-"English"
track.FU1cogs$RVLT_Delayed_Lang[track.FU1cogs$COG_REYII_LANG_TRF1=="fr"]<-"French"
track.FU1cogs$RVLT_Delayed_Score<-track.FU1cogs$COG_REYII_SCORE_TRF1
track.FU1cogs$RVLT_Delayed_Score[track.FU1cogs$RVLT_Delayed_Score<0]<-NA
track.FU1cogs1 <- track.FU1cogs[c(1,11:19)]
names(track.FU1cogs1) <-paste(names(track.FU1cogs1),"_1", sep="")
track.FU1cogs.Final<- rename(track.FU1cogs1, "ID" = "entity_id_1")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#FU2 (Non-Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_FUP2")
trackFU2<-read.csv("23SP001_McMaster_PRaina_FUP2_Trav1.csv")#Comprehensive Cohort Baseline
track.FU2<-trackFU2[c(1:5,47,1239,14,78,98,111,112,113,114,194,137,157,158,184,185,
191,195:202,50:54,685:694,415,419,514,517,732,389,401,423,604,427,
431,437,441,445,457,461,542,359,356,352,363,381,375,534,393,403,397,
538,407,367,530,371,568)]
#Sex (no variable in FU1)
SexFU2 <- track.FU2 %>%
left_join(track.BL, by = "entity_id") %>%
select(entity_id, Sex)
track.FU2$Sex<-SexFU2$Sex
#Age
track.FU2$Age<-track.FU2$AGE_NMBR_TRF2
#Marital Status
track.FU2$Relationship_status<-NA
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==1]<-"Single"
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==2]<-"Married"
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==3]<-"Widowed"
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==4]<-"Divorced"
track.FU2$Relationship_status[track.FU2$SDC_MRTL_TRF2==5]<-"Separated"
#Education 4 Category (No variable in FU2)
EducationFU2 <- track.FU2 %>%
left_join(track.BL, by = "entity_id") %>%
select(entity_id,Education4)
track.FU2$Education4<-EducationFU2$Education4
#Household Income
track.FU2$Income_Level<-NA
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==1]<-"<$20k"
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==2]<-"$20-50k"
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==3]<-"$50-100k"
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==4]<-"$100-150k"
track.FU2$Income_Level[track.FU2$INC_PTOT_TRF2==5]<-">$150k"
#Living Status
track.FU2$Living_status<-NA
track.FU2$Living_status[track.FU2$OWN_DWLG_TRF2==1]<-"House"
track.FU2$Living_status[track.FU2$OWN_DWLG_TRF2==2 |track.FU2$OWN_DWLG_TRF2==6]<-"Apartment/Condo/Townhome"
track.FU2$Living_status[track.FU2$OWN_DWLG_TRF2==3]<-"Assisted Living"
track.FU2$Living_status[track.FU2$OWN_DWLG_TRF2==4 | track.FU2$OWN_DWLG_TRF2==5 | track.FU2$OWN_DWLG_TRF2>=7]<-"Other"
#Alcohol (Based on a different question/scale)
track.FU2$Alcohol<-NA
track.FU2$Alcohol[track.FU2$ALC_FREQ_TRF2<=6]<-"Regular drinker (at least once a month)"
track.FU2$Alcohol[track.FU2$ALC_FREQ_TRF2==7]<-"Occasional drinker"
track.FU2$Alcohol[track.FU2$ALC_FREQ_TRF2==96]<-"Non-drinker"
#Smoking Status (no variable in FU2)
SmokingFU2 <- track.FU2 %>%
left_join(track.BL, by = "entity_id") %>%
select(entity_id,Smoking_Status)
track.FU2$Smoking_Status<-SmokingFU2$Smoking_Status
#Ethnicity (take from baseline))
EthnicityFU2 <- track.FU2 %>%
left_join(track.BL, by = "entity_id") %>%
select(entity_id,Ethnicity)
track.FU2$Ethnicity<-EthnicityFU2$Ethnicity
#############Physical Activity Scale for the Elderly######################
#Q1: Sitting Activity Frequency in Past 7 days
track.FU2$PASE_Q1<-NA
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2==1]<-0
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2==2]<-0.11
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2==3]<-0.25
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2==4]<-0.43
track.FU2$PASE_Q1[track.FU2$PA2_SIT_TRF2>4]<-NA
track.FU2$PASE_Q1[is.na(track.FU2$PA2_SIT_TRF2)]<-NA
track.FU2$PASE_Q1B<-NA
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==1]<-0
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==2]<-1
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==3]<-3
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==4]<-6
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2==5]<-10
track.FU2$PASE_Q1B[track.FU2$PA2_SITHR_SIT_TRF2>5]<- 0
track.FU2$PASE_Q1B[is.na(track.FU2$PA2_SITHR_SIT_TRF2)]<-NA
track.FU2$PASE_Q1B<-as.numeric(track.FU2$PASE_Q1B)
#Q2: Walking outside frequency
track.FU2$PASE_Q2<- NULL
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2==1]<-0
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2==2]<-0.11
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2==3]<-0.25
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2==4]<-0.43
track.FU2$PASE_Q2[track.FU2$PA2_WALK_TRF2>4]<-NA
track.FU2$PASE_Q2[is.na(track.FU2$PA2_WALK_TRF2)]<-NA
track.FU2$PASE_Q2A<- NA
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==1]<-0
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==2]<-1
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==3]<-3
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==4]<-6
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2==5]<-10
track.FU2$PASE_Q2A[track.FU2$PA2_WALKHR_TRF2>5]<-NA
track.FU2$PASE_Q2A[is.na(track.FU2$PA2_WALKHR_TRF2)]<-NA
track.FU2$PASE_Q2A<-as.numeric(track.FU2$PASE_Q2A)
#Q3: Light Sports or Activity Frequency
track.FU2$PASE_Q3<- NA
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2==1]<-0
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2==2]<-0.11
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2==3]<-0.25
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2==4]<-0.43
track.FU2$PASE_Q3[track.FU2$PA2_LSPRT_TRF2>4]<-NA
track.FU2$PASE_Q3[is.na(track.FU2$PA2_LSPRT_TRF2)]<-NA
track.FU2$PASE_Q3A<- NA
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==1]<-0
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==2]<-1
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==3]<-3
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==4]<-6
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2==5]<-10
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2>5]<-NA
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2 == 1 | is.na(track.FU2$PA2_LSPRTHR_TRF2)]<-0
track.FU2$PASE_Q3A[is.na(track.FU2$PA2_LSPRTHR_TRF2) & is.na(track.FU2$PA2_LSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q3A[track.FU2$PA2_LSPRTHR_TRF2>4 & is.na(track.FU2$PA2_LSPRTHR_TRF2)]<-NA
track.FU2$PASE_3A<-as.numeric(track.FU2$PASE_Q3A)
#Q4: Moderate Sports or Activity Frequency
track.FU2$PASE_Q4<-NA
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2==1]<-0
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2==2]<-0.11
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2==3]<-0.25
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2==4]<-0.43
track.FU2$PASE_Q4[track.FU2$PA2_MSPRT_TRF2>4]<-NA
track.FU2$PASE_Q4[is.na(track.FU2$PA2_MSPRT_TRF2)]<-NA
track.FU2$PASE_Q4A<- NA
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==1]<-0
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==2]<-1
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==3]<-3
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==4]<-6
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2==5]<-10
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRTHR_TRF2>5]<-NA
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRT_TRF2 == 1 | is.na(track.FU2$PA2_MSPRTHR_TRF2)]<-0
track.FU2$PASE_Q4A[is.na(track.FU2$PA2_MSPRT_TRF2) & is.na(track.FU2$PA2_MSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q4A[track.FU2$PA2_MSPRT_TRF2>4 & is.na(track.FU2$PA2_MSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q4A<-as.numeric(track.FU2$PASE_Q4A)
#Q5: Strenuous Sports or Activity Frequency
track.FU2$PASE_Q5<-NA
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2==1]<-0
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2==2]<-0.11
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2==3]<-0.25
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2==4]<-0.43
track.FU2$PASE_Q5[track.FU2$PA2_SSPRT_TRF2>4]<-NA
track.FU2$PASE_Q5[is.na(track.FU2$PA2_SSPRT_TRF2)]<-NA
track.FU2$PASE_Q5A<- NA
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==1]<-0
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==2]<-1
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==3]<-3
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==4]<-6
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2==5]<-10
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRTHR_TRF2>5]<-NA
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRT_TRF2 == 1 | is.na(track.FU2$PA2_SSPRTHR_TRF2)]<-0
track.FU2$PASE_Q5A[is.na(track.FU2$PA2_SSPRT_TRF2) & is.na(track.FU2$PA2_SSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q5A[track.FU2$PA2_SSPRT_TRF2>4 & is.na(track.FU2$PA2_SSPRTHR_TRF2)]<-NA
track.FU2$PASE_Q5A<-as.numeric(track.FU2$PASE_Q5A)
#Q6: Muscle strengthening and endurance exercise
track.FU2$PASE_Q6<-NA
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2==1]<-0
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2==2]<-0.11
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2==3]<-0.25
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2==4]<-0.43
track.FU2$PASE_Q6[track.FU2$PA2_EXER_TRF2>4]<-NA
track.FU2$PASE_Q6[is.na(track.FU2$PA2_EXER_TRF2)]<-NA
track.FU2$PASE_Q6A<- NA
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==1]<-0
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==2]<-1
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==3]<-3
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==4]<-6
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2==5]<-10
track.FU2$PASE_Q6A[track.FU2$PA2_EXERHR_TRF2>5]<-NA
track.FU2$PASE_Q6A[track.FU2$PA2_EXER_TRF2 == 1 | is.na(track.FU2$PA2_EXERHR_TRF2)]<-0
track.FU2$PASE_Q6A[is.na(track.FU2$PA2_EXER_TRF2) & is.na(track.FU2$PA2_EXERHR_TRF2)]<-NA
track.FU2$PASE_Q6A[track.FU2$PA2_EXER_TRF2>4 & is.na(track.FU2$PA2_EXERHR_TRF2)]<-NA
track.FU2$PASE_Q6A<-as.numeric(track.FU2$PASE_Q6A)
#Q7: Light Housework
track.FU2$PASE_Q7<-NA
track.FU2$PASE_Q7[track.FU2$PA2_LTHSWK_TRF2==1]<-1
track.FU2$PASE_Q7[track.FU2$PA2_LTHSWK_TRF2==2]<-0
track.FU2$PASE_Q7[track.FU2$PA2_LTHSWK_TRF2>2]<-NA
track.FU2$PASE_Q7[is.na(track.FU2$PA2_LTHSWK_TRF2)]<-NA
track.FU2$PASE_Q7<-as.numeric(track.FU2$PASE_Q7)
#Q8: Heavy Housework
track.FU2$PASE_Q8<-NA
track.FU2$PASE_Q8[track.FU2$PA2_HVYHSWK_TRF2==1]<-1
track.FU2$PASE_Q8[track.FU2$PA2_HVYHSWK_TRF2==2]<-0
track.FU2$PASE_Q8[track.FU2$PA2_HVYHSWK_TRF2>2]<-NA
track.FU2$PASE_Q8[is.na(track.FU2$PA2_HVYHSWK_TRF2)]<-NA
track.FU2$PASE_Q8<-as.numeric(track.FU2$PASE_Q8)
#Q9: Home Repair, Yardwork, Gardening, Care for another person
track.FU2$PASE_Q9A<-NA
track.FU2$PASE_Q9A[track.FU2$PA2_HMREPAIR_TRF2==1]<-1
track.FU2$PASE_Q9A[track.FU2$PA2_HMREPAIR_TRF2==2]<-0
track.FU2$PASE_Q9A[track.FU2$PA2_HMREPAIR_TRF2>2]<-NA
track.FU2$PASE_Q9A[is.na(track.FU2$PA2_HMREPAIR_TRF2)]<-NA
track.FU2$PASE_Q9A<-as.numeric(track.FU2$PASE_Q9A)
track.FU2$PASE_Q9B<-NA
track.FU2$PASE_Q9B[track.FU2$PA2_HVYODA_TRF2==1]<-1
track.FU2$PASE_Q9B[track.FU2$PA2_HVYODA_TRF2==2]<-0
track.FU2$PASE_Q9B[track.FU2$PA2_HVYODA_TRF2>2]<-NA
track.FU2$PASE_Q9B[is.na(track.FU2$PA2_HVYODA_TRF2)]<-NA
track.FU2$PASE_Q9B<-as.numeric(track.FU2$PASE_Q9B)
track.FU2$PASE_Q9C<-NA
track.FU2$PASE_Q9C[track.FU2$PA2_LTODA_TRF2==1]<-1
track.FU2$PASE_Q9C[track.FU2$PA2_LTODA_TRF2==2]<-0
track.FU2$PASE_Q9C[track.FU2$PA2_LTODA_TRF2>2]<-NA
track.FU2$PASE_Q9C[is.na(track.FU2$PA2_LTODA_TRF2)]<-NA
track.FU2$PASE_Q9C<-as.numeric(track.FU2$PASE_Q9C)
track.FU2$PASE_Q9D<-NA
track.FU2$PASE_Q9D[track.FU2$PA2_CRPRSN_TRF2==1]<-1
track.FU2$PASE_Q9D[track.FU2$PA2_CRPRSN_TRF2==2]<-0
track.FU2$PASE_Q9D[track.FU2$PA2_CRPRSN_TRF2>2]<-NA
track.FU2$PASE_Q9D[is.na(track.FU2$PA2_CRPRSN_TRF2)]<-NA
track.FU2$PASE_Q9D<-as.numeric(track.FU2$PASE_Q9D)
#Q10: Working and Volunteering
track.FU2$PASE_Q10<-NA
track.FU2$PASE_Q10[track.FU2$PA2_WRK_TRF2==1]<-1
track.FU2$PASE_Q10[track.FU2$PA2_WRK_TRF2==2]<-0
track.FU2$PASE_Q10[track.FU2$PA2_WRK_TRF2>2]<-NA
track.FU2$PASE_Q10[is.na(track.FU2$PA2_WRK_TRF2)]<-NA
track.FU2$PASE_Q10<-as.numeric(track.FU2$PASE_Q10)
track.FU2$PASE_Q10A<-NA
track.FU2$PASE_Q10A<-track.FU2$PA2_WRKHRS_NB_TRF2
track.FU2$PASE_Q10A[track.FU2$PA2_WRKHRS_NB_TRF2>=700]<-NA
track.FU2$PASE_Q10A[track.FU2$PA2_WRK_TRF2 == 2 | is.na(track.FU2$PA2_WRKHRS_NB_TRF2)]<-0
track.FU2$PASE_Q10A[is.na(track.FU2$PA2_WRK_TRF2) & is.na(track.FU2$PA2_WRKHRS_NB_TRF2)]<-NA
track.FU2$PASE_Q10A<-as.numeric(track.FU2$PASE_Q10A)
track.FU2$PASE_Q10A<-track.FU2$PASE_Q10A/7
#PASE TOTAL SCORE#
track.FU2$PASE_TOTAL<-track.FU2$PASE_Q2*track.FU2$PASE_Q2A*20 + track.FU2$PASE_Q3*track.FU2$PASE_Q3A*21 + track.FU2$PASE_Q4*track.FU2$PASE_Q4A*23 + track.FU2$PASE_Q5*track.FU2$PASE_Q5A*30 +
track.FU2$PASE_Q6*track.FU2$PASE_Q6A*30 + (track.FU2$PASE_Q7+track.FU2$PASE_Q8)*25 + track.FU2$PASE_Q9A*30 + track.FU2$PASE_Q9B*36 + track.FU2$PASE_Q9C*20 + track.FU2$PASE_Q9D*35 +
track.FU2$PASE_Q10*track.FU2$PASE_Q10A*21
#BMI (Not included)
track.FU2$Height<-NA
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==1]<-36
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==2]<-37
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==3]<-38
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==4]<-39
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==5]<-40
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==6]<-41
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==7]<-42
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==8]<-43
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==9]<-44
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==10]<-45
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==11]<-46
track.FU2$Height[track.FU2$HWT_HGHT3_TRF2==12]<-47
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==1]<-48
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==2]<-49
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==3]<-50
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==4]<-51
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==5]<-52
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==6]<-53
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==7]<-54
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==8]<-55
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==9]<-56
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==10]<-57
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==11]<-58
track.FU2$Height[track.FU2$HWT_HGHT4_TRF2==12]<-59
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==1]<-60
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==2]<-61
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==3]<-62
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==4]<-63
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==5]<-64
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==6]<-65
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==7]<-66
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==8]<-67
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==9]<-68
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==10]<-69
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==11]<-70
track.FU2$Height[track.FU2$HWT_HGHT5_TRF2==12]<-71
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==1]<-72
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==2]<-73
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==3]<-74
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==4]<-75
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==5]<-76
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==6]<-77
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==7]<-78
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==8]<-79
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==9]<-80
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==10]<-81
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==11]<-82
track.FU2$Height[track.FU2$HWT_HGHT6_TRF2==12]<-83
track.FU2$Height<-as.numeric(track.FU2$Height)*0.0254
track.FU2$Weight<-track.FU2$HWT_WGHT_NB_TRF2
track.FU2$Weight[track.FU2$HWT_WGHT_NB_TRF2>500]<-NA
track.FU2$Weight[track.FU2$HWT_WGHT_NB_TRF2==-8888]<-NA
track.FU2$BMI<-track.FU2$Weight/((track.FU2$Height)^2)
#CESD-10 (NO CESD variable in the data set; need to calculate by hand)
#Q1: I was bothered by things that usually don’t bother me.
track.FU2$CESD_Q1<-NA
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2==1]<-3
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2==2]<-2
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2==3]<-1
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2==4]<-0
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2>4]<-NA
track.FU2$CESD_Q1[track.FU2$DEP_BOTR_TRF2<0]<-NA
#Q2: I had trouble keeping my mind on what I was doing.
track.FU2$CESD_Q2<-NA
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2==1]<-3
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2==2]<-2
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2==3]<-1
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2==4]<-0
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2>4]<-NA
track.FU2$CESD_Q2[track.FU2$DEP_MIND_TRF2<0]<-NA
#Q3: I felt depressed
track.FU2$CESD_Q3<-NA
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2==1]<-3
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2==2]<-2
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2==3]<-1
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2==4]<-0
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2>4]<-NA
track.FU2$CESD_Q3[track.FU2$DEP_FLDP_TRF2<0]<-NA
#Q4: I felt that everything I did was an effort.
track.FU2$CESD_Q4<-NA
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2==1]<-3
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2==2]<-2
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2==3]<-1
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2==4]<-0
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2>4]<-NA
track.FU2$CESD_Q4[track.FU2$DEP_FFRT_TRF2<0]<-NA
#Q5: I felt hopeful about the future
track.FU2$CESD_Q5<-NA
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2==1]<-0
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2==2]<-1
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2==3]<-2
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2==4]<-3
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2>4]<-NA
track.FU2$CESD_Q5[track.FU2$DEP_HPFL_TRF2<0]<-NA
#Q6: I felt fearful.
track.FU2$CESD_Q6<-NA
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2==1]<-3
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2==2]<-2
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2==3]<-1
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2==4]<-0
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2>4]<-NA
track.FU2$CESD_Q6[track.FU2$DEP_FRFL_TRF2<0]<-NA
#Q7: My sleep was restless
track.FU2$CESD_Q7<-NA
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2==1]<-3
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2==2]<-2
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2==3]<-1
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2==4]<-0
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2>4]<-NA
track.FU2$CESD_Q7[track.FU2$DEP_RSTLS_TRF2<0]<-NA
#Q8: I was happy
track.FU2$CESD_Q8<-NA
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2==1]<-0
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2==2]<-1
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2==3]<-2
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2==4]<-3
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2>4]<-NA
track.FU2$CESD_Q8[track.FU2$DEP_HAPP_TRF2<0]<-NA
#Q9: I felt lonely
track.FU2$CESD_Q9<-NA
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2==1]<-3
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2==2]<-2
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2==3]<-1
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2==4]<-0
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2>4]<-NA
track.FU2$CESD_Q9[track.FU2$DEP_LONLY_TRF2<0]<-NA
#Q10: I could not “get going.”
track.FU2$CESD_Q10<-NA
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2==1]<-3
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2==2]<-2
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2==3]<-1
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2==4]<-0
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2>4]<-NA
track.FU2$CESD_Q10[track.FU2$DEP_GTGO_TRF2<0]<-NA
track.FU2$CESD_10<-track.FU2$CESD_Q1 + track.FU2$CESD_Q2 + track.FU2$CESD_Q3 + track.FU2$CESD_Q4 + track.FU2$CESD_Q5 +
track.FU2$CESD_Q6 + track.FU2$CESD_Q7 + track.FU2$CESD_Q8 + track.FU2$CESD_Q9 + track.FU2$CESD_Q10
#Subjective Cognitive Impairment
track.FU2$SCI<- NA
track.FU2$SCI[track.FU2$CCT_MEMPB_TRF2==1]<- "Yes"
track.FU2$SCI[track.FU2$CCT_MEMPB_TRF2==2]<- "No"
#Dementia and AD
track.FU2$Dementia<- NA
track.FU2$Dementia[track.FU2$CCT_ALZH_TRF2==1]<- "Yes"
track.FU2$Dementia[track.FU2$CCT_ALZH_TRF2==2]<- "No"
#Anxiety
track.FU2$Anxiety<- NA
track.FU2$Anxiety[track.FU2$CCT_ANXI_TRF2==1]<- "Yes"
track.FU2$Anxiety[track.FU2$CCT_ANXI_TRF2==2]<- "No"
#Mood Disorders
track.FU2$Mood_Disord<- NA
track.FU2$Mood_Disord[track.FU2$CCT_MOOD_TRF2==1]<- "Yes"
track.FU2$Mood_Disord[track.FU2$CCT_MOOD_TRF2==2]<- "No"
#Number of Chronic Conditions
track.FU2$Chronic_conditions<-NA
#Pet Ownership at Baseline
track.FU2$Pet_Owner<-NA
track.FU2$Pet_Owner[track.FU2$SSA_PET_TRF2==1]<-"Yes"
track.FU2$Pet_Owner[track.FU2$SSA_PET_TRF2==2]<-"No"
track.FU2$CCT_HEART_TRM[track.FU2$CCT_HEART_TRF2==1]<- 1 #Heart Disease
track.FU2$CCT_HEART_TRM[track.FU2$CCT_HEART_TRF2==2]<- 0
track.FU2$CCT_HEART_TRM[track.FU2$CCT_HEART_TRF2==8]<- NA
track.FU2$CCT_HEART_TRM[track.FU2$CCT_HEART_TRF2==9]<- NA
track.FU2$CCT_PVD_TRM[track.FU2$CCT_PAD_TRF2==1]<- 1 #peripheral vascular disease (called Peripheral Artery Disease, unlike Baseline or FU1)
track.FU2$CCT_PVD_TRM[track.FU2$CCT_PAD_TRF2==2]<- 0
track.FU2$CCT_PVD_TRM[track.FU2$CCT_PAD_TRF2==8]<- NA
track.FU2$CCT_PVD_TRM[track.FU2$CCT_PAD_TRF2==9]<- NA
track.FU2$CCT_MEMPB_TRM[track.FU2$CCT_MEMPB_TRF2==1]<- 1 #SCI
track.FU2$CCT_MEMPB_TRM[track.FU2$CCT_MEMPB_TRF2==2]<- 0
track.FU2$CCT_MEMPB_TRM[track.FU2$CCT_MEMPB_TRF2==8]<- NA
track.FU2$CCT_MEMPB_TRM[track.FU2$CCT_MEMPB_TRF2==9]<- NA
track.FU2$CCT_ALZH_TRM[track.FU2$CCT_ALZH_TRF2==1]<- 1 #Alzheimers or demeinta
track.FU2$CCT_ALZH_TRM[track.FU2$CCT_ALZH_TRF2==2]<- 0
track.FU2$CCT_ALZH_TRM[track.FU2$CCT_ALZH_TRF2==8]<- NA
track.FU2$CCT_ALZH_TRM[track.FU2$CCT_ALZH_TRF2==9]<- NA
track.FU2$CCT_MS_TRM[track.FU2$CCT_MS_TRF2==1]<- 1 #Multiple sclerosis
track.FU2$CCT_MS_TRM[track.FU2$CCT_MS_TRF2==2]<- 0
track.FU2$CCT_MS_TRM[track.FU2$CCT_MS_TRF2==8]<- NA
track.FU2$CCT_MS_TRM[track.FU2$CCT_MS_TRF2==9]<- NA
track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==1]<- 1 #Epilepsy (Different question)
track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==2]<- 0
track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==3]<- NA
track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==8]<- NA
track.FU2$CCT_EPIL_TRM[track.FU2$EPI_EVER_TRF2==9]<- NA
track.FU2$CCT_MGRN_TRM[track.FU2$CCT_MGRN_TRF2==1]<- 1 #Migraine headaches
track.FU2$CCT_MGRN_TRM[track.FU2$CCT_MGRN_TRF2==2]<- 0
track.FU2$CCT_MGRN_TRM[track.FU2$CCT_MGRN_TRF2==8]<- NA
track.FU2$CCT_MGRN_TRM[track.FU2$CCT_MGRN_TRF2==9]<- NA
track.FU2$CCT_ULCR_TRM[track.FU2$CCT_ULCR_TRF2==1]<- 1 #Intenstinal or stomach ulcers
track.FU2$CCT_ULCR_TRM[track.FU2$CCT_ULCR_TRF2==2]<- 0
track.FU2$CCT_ULCR_TRM[track.FU2$CCT_ULCR_TRF2==8]<- NA
track.FU2$CCT_ULCR_TRM[track.FU2$CCT_ULCR_TRF2==9]<- NA
track.FU2$CCT_IBDIBS_TRM[track.FU2$CCT_IBSYD_TRF2==1]<- 1 #Bowel disorder (different name for IBS and IBD)
track.FU2$CCT_IBDIBS_TRM[track.FU2$CCT_IBSYD_TRF2==2]<- 0
track.FU2$CCT_IBDIBS_TRM[track.FU2$CCT_IBSYD_TRF2==8]<- NA
track.FU2$CCT_IBDIBS_TRM[track.FU2$CCT_IBSYD_TRF2==9]<- NA
track.FU2$CCT_BOWINC_TRM[track.FU2$CCT_BOWINC_TRF2==1]<- 1 #Bowel incontinence
track.FU2$CCT_BOWINC_TRM[track.FU2$CCT_BOWINC_TRF2==2]<- 0
track.FU2$CCT_BOWINC_TRM[track.FU2$CCT_BOWINC_TRF2==8]<- NA
track.FU2$CCT_BOWINC_TRM[track.FU2$CCT_BOWINC_TRF2==9]<- NA
track.FU2$CCT_URIINC_TRM[track.FU2$CCT_URIINC_TRF2==1]<- 1 #Urinary incontinence
track.FU2$CCT_URIINC_TRM[track.FU2$CCT_URIINC_TRF2==2]<- 0
track.FU2$CCT_URIINC_TRM[track.FU2$CCT_URIINC_TRF2==8]<- NA
track.FU2$CCT_URIINC_TRM[track.FU2$CCT_URIINC_TRF2==9]<- NA
track.FU2$CCT_MACDEG_TRM[track.FU2$CCT_MACDEG_TRF2==1]<- 1 #Macular degeneration
track.FU2$CCT_MACDEG_TRM[track.FU2$CCT_MACDEG_TRF2==2]<- 0
track.FU2$CCT_MACDEG_TRM[track.FU2$CCT_MACDEG_TRF2==8]<- NA
track.FU2$CCT_MACDEG_TRM[track.FU2$CCT_MACDEG_TRF2==9]<- NA
track.FU2$CCT_CANC_TRM[track.FU2$CCT_CANC_TRF2==1]<- 1 #All-cause cancer
track.FU2$CCT_CANC_TRM[track.FU2$CCT_CANC_TRF2==2]<- 0
track.FU2$CCT_CANC_TRM[track.FU2$CCT_CANC_TRF2==8]<- NA
track.FU2$CCT_CANC_TRM[track.FU2$CCT_CANC_TRF2==9]<- NA
track.FU2$CCT_BCKP_TRM<-NA #Back problems but not fibromyalgia or arthritis (Question not included)
track.FU2$CCT_KIDN_TRM[track.FU2$CCT_KIDN_TRF2==1]<- 1 #Kidney disease
track.FU2$CCT_KIDN_TRM[track.FU2$CCT_KIDN_TRF2==2]<- 0
track.FU2$CCT_KIDN_TRM[track.FU2$CCT_KIDN_TRF2==8]<- NA
track.FU2$CCT_KIDN_TRM[track.FU2$CCT_KIDN_TRF2==9]<- NA
track.FU2$CCT_OTCCT_TRM <- NA #Other long term mental or physical condition (Question not included)
track.FU2$CCT_OAHAND_TRM[track.FU2$CCT_OAHAND_TRF2==1]<- 1 #Hand arthritis
track.FU2$CCT_OAHAND_TRM[track.FU2$CCT_OAHAND_TRF2==2]<- 0
track.FU2$CCT_OAHAND_TRM[track.FU2$CCT_OAHAND_TRF2==8]<- NA
track.FU2$CCT_OAHAND_TRM[track.FU2$CCT_OAHAND_TRF2==9]<- NA
track.FU2$CCT_OAHIP_TRM[track.FU2$CCT_OAHIP_TRF2==1]<- 1 #Hip arthritis
track.FU2$CCT_OAHIP_TRM[track.FU2$CCT_OAHIP_TRF2==2]<- 0
track.FU2$CCT_OAHIP_TRM[track.FU2$CCT_OAHIP_TRF2==8]<- NA
track.FU2$CCT_OAHIP_TRM[track.FU2$CCT_OAHIP_TRF2==9]<- NA
track.FU2$CCT_OAKNEE_TRM[track.FU2$CCT_OAKNEE_TRF2==1]<- 1 #Knee arthritis
track.FU2$CCT_OAKNEE_TRM[track.FU2$CCT_OAKNEE_TRF2==2]<- 0
track.FU2$CCT_OAKNEE_TRM[track.FU2$CCT_OAKNEE_TRF2==8]<- NA
track.FU2$CCT_OAKNEE_TRM[track.FU2$CCT_OAKNEE_TRF2==9]<- NA
track.FU2$CCT_RA_TRM[track.FU2$CCT_RA_TRF2==1]<- 1 #Rheumatoid arthritis
track.FU2$CCT_RA_TRM[track.FU2$CCT_RA_TRF2==2]<- 0
track.FU2$CCT_RA_TRM[track.FU2$CCT_RA_TRF2==8]<- NA
track.FU2$CCT_RA_TRM[track.FU2$CCT_RA_TRF2==9]<- NA
track.FU2$CCT_ARTOT_TRM <- NA #Other arthritis (not included in the data)
track.FU2$DIA_DIAB_TRM[track.FU2$CCT_DIAB_TRF2==1]<- 1 #Diabetes
track.FU2$DIA_DIAB_TRM[track.FU2$CCT_DIAB_TRF2==2]<- 0
track.FU2$DIA_DIAB_TRM[track.FU2$CCT_DIAB_TRF2==8]<- NA
track.FU2$DIA_DIAB_TRM[track.FU2$CCT_DIAB_TRF2==9]<- NA
track.FU2$CCT_HBP_TRM[track.FU2$CCT_HBP_TRF2==1]<- 1 #High blood pressure
track.FU2$CCT_HBP_TRM[track.FU2$CCT_HBP_TRF2==2]<- 0
track.FU2$CCT_HBP_TRM[track.FU2$CCT_HBP_TRF2==8]<- NA
track.FU2$CCT_HBP_TRM[track.FU2$CCT_HBP_TRF2==9]<- NA
track.FU2$CCT_UTHYR_TRM[track.FU2$CCT_UTHYR_TRF2==1]<- 1 #Under active thyroid
track.FU2$CCT_UTHYR_TRM[track.FU2$CCT_UTHYR_TRF2==2]<- 0
track.FU2$CCT_UTHYR_TRM[track.FU2$CCT_UTHYR_TRF2==8]<- NA
track.FU2$CCT_UTHYR_TRM[track.FU2$CCT_UTHYR_TRF2==9]<- NA
track.FU2$CCT_ANGI_TRM[track.FU2$CCT_ANGI_TRF2==1]<- 1 #Angina
track.FU2$CCT_ANGI_TRM[track.FU2$CCT_ANGI_TRF2==2]<- 0
track.FU2$CCT_ANGI_TRM[track.FU2$CCT_ANGI_TRF2==8]<- NA
track.FU2$CCT_ANGI_TRM[track.FU2$CCT_ANGI_TRF2==9]<- NA
track.FU2$CCT_CVA_TRM[track.FU2$CCT_CVA_TRF2==1]<- 1 #Stroke or CVA
track.FU2$CCT_CVA_TRM[track.FU2$CCT_CVA_TRF2==2]<- 0
track.FU2$CCT_CVA_TRM[track.FU2$CCT_CVA_TRF2==8]<- NA
track.FU2$CCT_CVA_TRM[track.FU2$CCT_CVA_TRF2==9]<- NA
track.FU2$CCT_AMI_TRM[track.FU2$CCT_AMI_TRF2==1]<- 1 #myocardial infarction
track.FU2$CCT_AMI_TRM[track.FU2$CCT_AMI_TRF2==2]<- 0
track.FU2$CCT_AMI_TRM[track.FU2$CCT_AMI_TRF2==8]<- NA
track.FU2$CCT_AMI_TRM[track.FU2$CCT_AMI_TRF2==9]<- NA
track.FU2$CCT_OTHYR_TRM[track.FU2$CCT_OTHYR_TRF2==1]<- 1 #Overactive thyroid
track.FU2$CCT_OTHYR_TRM[track.FU2$CCT_OTHYR_TRF2==2]<- 0
track.FU2$CCT_OTHYR_TRM[track.FU2$CCT_OTHYR_TRF2==8]<- NA
track.FU2$CCT_OTHYR_TRM[track.FU2$CCT_OTHYR_TRF2==9]<- NA
track.FU2$CCT_TIA_TRM[track.FU2$CCT_TIA_TRF2==1]<- 1 #Transient Ischemic Attack
track.FU2$CCT_TIA_TRM[track.FU2$CCT_TIA_TRF2==2]<- 0
track.FU2$CCT_TIA_TRM[track.FU2$CCT_TIA_TRF2==8]<- NA
track.FU2$CCT_TIA_TRM[track.FU2$CCT_TIA_TRF2==9]<- NA
track.FU2$CCT_ASTHM_TRM[track.FU2$CCT_ASTHM_TRF2==1]<- 1 #Asthma
track.FU2$CCT_ASTHM_TRM[track.FU2$CCT_ASTHM_TRF2==2]<- 0
track.FU2$CCT_ASTHM_TRM[track.FU2$CCT_ASTHM_TRF2==8]<- NA
track.FU2$CCT_ASTHM_TRM[track.FU2$CCT_ASTHM_TRF2==9]<- NA
track.FU2$CCT_OSTPO_TRM[track.FU2$CCT_OSTPO_TRF2==1]<- 1 #Osteoperosis
track.FU2$CCT_OSTPO_TRM[track.FU2$CCT_OSTPO_TRF2==2]<- 0
track.FU2$CCT_OSTPO_TRM[track.FU2$CCT_OSTPO_TRF2==8]<- NA
track.FU2$CCT_OSTPO_TRM[track.FU2$CCT_OSTPO_TRF2==9]<- NA
track.FU2$CCT_PARK_TRM[track.FU2$PKD_PARK_TRF2==1]<- 1 #Parkinsons
track.FU2$CCT_PARK_TRM[track.FU2$PKD_PARK_TRF2==2]<- 0
track.FU2$CCT_PARK_TRM[track.FU2$PKD_PARK_TRF2==8]<- NA
track.FU2$CCT_PARK_TRM[track.FU2$PKD_PARK_TRF2==9]<- NA
track.FU2$CCT_COPD_TRM[track.FU2$CCT_COPD_TRF2==1]<- 1 #COPD
track.FU2$CCT_COPD_TRM[track.FU2$CCT_COPD_TRF2==2]<- 0
track.FU2$CCT_COPD_TRM[track.FU2$CCT_COPD_TRF2==8]<- NA
track.FU2$CCT_COPD_TRM[track.FU2$CCT_COPD_TRF2==9]<- NA
track.FU2$Chronic_conditions<-track.FU2$CCT_HEART_TRM + track.FU2$CCT_PVD_TRM + track.FU2$CCT_MEMPB_TRM + track.FU2$CCT_ALZH_TRM + track.FU2$CCT_MS_TRM +
track.FU2$CCT_EPIL_TRM + track.FU2$CCT_MGRN_TRM + track.FU2$CCT_ULCR_TRM +
track.FU2$CCT_IBDIBS_TRM + track.FU2$CCT_BOWINC_TRM + track.FU2$CCT_URIINC_TRM + track.FU2$CCT_MACDEG_TRM + track.FU2$CCT_CANC_TRM + track.FU2$CCT_KIDN_TRM +
track.FU2$CCT_OAHAND_TRM + track.FU2$CCT_OAHIP_TRM + track.FU2$CCT_OAKNEE_TRM + track.FU2$CCT_RA_TRM +
track.FU2$DIA_DIAB_TRM + track.FU2$CCT_HBP_TRM + track.FU2$CCT_UTHYR_TRM + track.FU2$CCT_ANGI_TRM + track.FU2$CCT_CVA_TRM + track.FU2$CCT_AMI_TRM + track.FU2$CCT_OTHYR_TRM +
track.FU2$CCT_TIA_TRM + track.FU2$CCT_ASTHM_TRM + track.FU2$CCT_OSTPO_TRM + track.FU2$CCT_PARK_TRM + track.FU2$CCT_COPD_TRM
#Restless Sleep (≥ 3-4 days/week)
track.FU2$RSTLS_Sleep<-NA
track.FU2$RSTLS_Sleep[track.FU2$DEP_RSTLS_TRF2<3]<-1
track.FU2$RSTLS_Sleep[track.FU2$DEP_RSTLS_TRF2>=3 & track.FU2$DEP_RSTLS_TRF2<8]<-0
track.FU2$RSTLS_Sleep[track.FU2$DEP_RSTLS_TRF2>4]<-NA
#Finalize data set
track.FU2.1<-track.FU2[c(1:3,78:86,108,111,122:128,88,162)]
names(track.FU2.1) <-paste(names(track.FU2.1),"_2", sep="")
track.FU2.Final<- rename(track.FU2.1, "ID" = "entity_id_2")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#FU2 (Cogs)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
setwd("~/Desktop/UBC-Postdoctoral Fellowship/CLSA - COVID Brain/Data/23SP001_McMaster_PRaina_FUP2_COG")
trackFU2cogs<-read.csv("23SP001_McMaster_PRaina_Cognition_Tra_FUP2_v0.csv")#Comprehensive Cohort Baseline
track.FU2cogs<-trackFU2cogs[c(1,64:66,110,109,41,42,48,47)]
############Cognitive Function##############
#~~~~~~Animal Fluency~~~~~~~~~~~#
track.FU2cogs$Animal_Fluency_Strict<-track.FU2cogs$COG_AFT_SCORE_1_TRF2
track.FU2cogs$Animal_Fluency_Lenient<-track.FU2cogs$COG_AFT_SCORE_2_TRF2
track.FU2cogs$Animal_Fluency_Lenient[track.FU2cogs$Animal_Fluency_Lenient<0]<-NA
track.FU2cogs$Animal_Fluency_Strict[track.FU2cogs$Animal_Fluency_Strict<0]<-NA
track.FU2cogs$Animal_Fluency_Lang<-NA
track.FU2cogs$Animal_Fluency_Lang[track.FU2cogs$COG_AFT_STARTLANG_TRF2=="en"]<-"English"
track.FU2cogs$Animal_Fluency_Lang[track.FU2cogs$COG_AFT_STARTLANG_TRF2=="fr"]<-"French"
#~~~~~~~~Mental Alteration Test~~~~~~~~~~#
track.FU2cogs$MAT_Lang<-NA
track.FU2cogs$MAT_Lang[track.FU2cogs$COG_MAT_STARTLANG_TRF2=="en"]<-"English"
track.FU2cogs$MAT_Lang[track.FU2cogs$COG_MAT_STARTLANG_TRF2=="fr"]<-"French"
track.FU2cogs$MAT_Score<-track.FU2cogs$COG_MAT_SCORE_TRF2
track.FU2cogs$MAT_Score[track.FU2cogs$MAT_Score<0]<-NA
#~~~~~~~~RVLT~~~~~~~~~~~~~~~~#
#Rey-Immediate Recall
track.FU2cogs$RVLT_Immediate_Lang<- NA
track.FU2cogs$RVLT_Immediate_Lang[track.FU2cogs$COG_REYI_STARTLANG_TRF2=="en"]<-"English"
track.FU2cogs$RVLT_Immediate_Lang[track.FU2cogs$COG_REYI_STARTLANG_TRF2=="fr"]<-"French"
track.FU2cogs$RVLT_Immediate_Score<-track.FU2cogs$COG_REYI_SCORE_TRF2
track.FU2cogs$RVLT_Immediate_Score[track.FU2cogs$RVLT_Immediate_Score<0]<-NA
#Rey-Delayed Recall
track.FU2cogs$RVLT_Delayed_Lang<- NA
track.FU2cogs$RVLT_Delayed_Lang[track.FU2cogs$COG_REYII_STARTLANG_TRF2=="en"]<-"English"
track.FU2cogs$RVLT_Delayed_Lang[track.FU2cogs$COG_REYII_STARTLANG_TRF2=="fr"]<-"French"
track.FU2cogs$RVLT_Delayed_Score<-track.FU2cogs$COG_REYII_SCORE_TRF2
track.FU2cogs$RVLT_Delayed_Score[track.FU2cogs$RVLT_Delayed_Score<0]<-NA
track.FU2cogs1 <- track.FU2cogs[c(1,11:19)]
names(track.FU2cogs1) <-paste(names(track.FU2cogs1),"_2", sep="")
track.FU2cogs.Final<- rename(track.FU2cogs1, "ID" = "entity_id_2")
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#Combine Datasets
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#Combine Baseline dataframes
Track.Baseline.Final <- merge(track.BL.Final, track.BLcogs.Final, by = "ID")
#Combine FU1 dataframes
Track.FU1.Final <- merge(track.FU1.Final, track.FU1cogs.Final, by = "ID")
#Combine FU2 dataframes
Track.FU2.Final <- merge(track.FU2.Final, track.FU2cogs.Final, by = "ID")
#Combine data across different time points into full data set
Track.BL.FU1 <- merge(Track.Baseline.Final, Track.FU1.Final, by = "ID")
Track.Full <- merge(Track.BL.FU1, Track.FU2.Final, by = "ID")
This section details how our final data-set was developed. Our final data set included cognitively healthy participants at baseline, FU1, and FU2 with complete baseline neuropsycholoigcal testing (including education level, which was necessary for standardized scores). Thus our final sample size included in our linear mixed models was N=11,355.
Track.Final_Data <- subset(Track.Full, SCI_0=="No" & Dementia_0=="No" & SCI_1=="No" & Dementia_1 =="No" &
SCI_2=="No" & Dementia_2 == "No" & !is.na(Animal_Fluency_Strict_0) & !is.na(MAT_Score_0) & !is.na(Education4_0) &
!is.na(RVLT_Immediate_Score_0) & !is.na(RVLT_Delayed_Score_0))
We then normalized all cognitive variables for language, age, and biological sex. Score standardization is dependent upon the following steps:
Tracking.Adjusted_Full <- Track.Final_Data %>%
mutate(
#Baseline Scores
#RVLT Immediate Score
RVLT_Immediate_Predicted_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 7.768 - 0.050*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 7.449 - 0.036*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ 10.095 - 0.073*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ 9.686 - 0.064*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 8.077 - 0.043*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ 9.806 - 0.059*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ 9.161 - 0.047*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ 9.804 - 0.053*Age_0,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 5.666 - 0.025*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 8.953 - 0.067*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ 7.662 - 0.039*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ 8.829 - 0.057*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 6.976 - 0.031*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ 8.667 - 0.045*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ 9.502 - 0.061*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ 9.013 - 0.047*Age_0
),
RVLT_Immediate_Residual_0 = RVLT_Immediate_Score_0 - RVLT_Immediate_Predicted_0,
RVLT_Immediate_Z_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.471,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.525,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.611,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.675,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.528,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.643,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.694,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="English" ~ RVLT_Immediate_Residual_0/1.802,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.290,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.473,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.913,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.641,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.623,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.595,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.605,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Immediate_Lang_0=="French" ~ RVLT_Immediate_Residual_0/1.715
),
RVLT_Immediate_Normed_0 = RVLT_Immediate_Z_0*3 +10,
RVLT_Immediate_Normed_0 = if_else(RVLT_Immediate_Normed_0 < 0, 0.01, RVLT_Immediate_Normed_0),
#RVLT Delayed Score
RVLT_Delayed_Predicted_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 6.628 - 0.062*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 6.851 - 0.058*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ 8.289 - 0.076*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ 8.165 - 0.070*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 7.163 - 0.055*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ 8.115 - 0.063*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ 8.151 - 0.059*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ 8.844 - 0.066*Age_0,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 4.802 - 0.036*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 8.219 - 0.083*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ 9.721 - 0.097*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ 7.048 - 0.055*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 6.280 - 0.044*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ 6.999 - 0.042*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ 9.081 - 0.074*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ 8.712 - 0.066*Age_0
),
RVLT_Delayed_Residual_0 = RVLT_Delayed_Score_0 - RVLT_Delayed_Predicted_0,
RVLT_Delayed_Z_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.534,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.739,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.802,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.890,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.787,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/2.005,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/1.869,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="English" ~ RVLT_Delayed_Residual_0/2.135,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.559,
Sex_0 =="M" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.571,
Sex_0 =="M" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.815,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.721,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.859,
Sex_0 =="F" & Education4_0 == "High School Diploma" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.793,
Sex_0 =="F" & Education4_0 == "Some College" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.901,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & RVLT_Delayed_Lang_0=="French" ~ RVLT_Delayed_Residual_0/1.890
),
RVLT_Delayed_Normed_0 = RVLT_Delayed_Z_0*3 +10,
RVLT_Delayed_Normed_0 = if_else(RVLT_Delayed_Normed_0 < 0, 0.01, RVLT_Delayed_Normed_0),
#Animal Fluency
Animal_Fluency_Predicted_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 23.132 - 0.095*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 28.923 - 0.157*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ 32.513 - 0.202*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ 31.143 - 0.168*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 23.433 - 0.114*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ 29.912 - 0.181*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ 30.764 - 0.178*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ 32.003 - 0.186*Age_0,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 26.034 - 0.152*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 33.358 - 0.241*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ 36.511 - 0.277*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ 30.193 - 0.179*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 21.460 - 0.089*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ 21.355 - 0.070*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ 30.881 - 0.205*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ 29.961 - 0.180*Age_0
),
Animal_Fluency_Residual_0 = Animal_Fluency_Strict_0 - Animal_Fluency_Predicted_0,
Animal_Fluency_Z_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.145,
Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.348,
Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.163,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.354,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/4.665,
Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/4.728,
Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.176,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="English" ~ Animal_Fluency_Residual_0/5.369,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/3.911,
Sex_0 =="M" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.889,
Sex_0 =="M" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/5.061,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.869,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.178,
Sex_0 =="F" & Education4_0 == "High School Diploma" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.321,
Sex_0 =="F" & Education4_0 == "Some College" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.468,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & Animal_Fluency_Lang_0=="French" ~ Animal_Fluency_Residual_0/4.940
),
Animal_Fluency_Normed_0 = Animal_Fluency_Z_0*3 +10,
Animal_Fluency_Normed_0 = if_else(Animal_Fluency_Normed_0 < 0, 0.01, Animal_Fluency_Normed_0),
#Mental Alteration Test
MAT_Predicted_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ 33.295 - 0.161*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ 34.074 - 0.123*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ 41.488 - 0.219*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ 40.573 - 0.190*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ 39.102 - 0.251*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ 41.657 - 0.246*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ 36.877 - 0.168*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ 38.849 - 0.188*Age_0,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ 36.630 - 0.252*Age_0,
Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ 38.784 - 0.181*Age_0,
Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ 51.105 - 0.381*Age_0,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ 44.106 - 0.257*Age_0,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ 34.814 - 0.214*Age_0,
Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ 38.756 - 0.202*Age_0,
Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ 47.024 - 0.315*Age_0,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ 41.717 - 0.234*Age_0
),
MAT_Residual_0 = MAT_Score_0 - MAT_Predicted_0,
MAT_Z_0 = case_when(
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.602,
Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.702,
Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.490,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.727,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.080,
Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="English" ~ MAT_Residual_0/7.139,
Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="English" ~ MAT_Residual_0/6.915,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="English" ~ MAT_Residual_0/6.979,
Sex_0 =="M" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.589,
Sex_0 =="M" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.234,
Sex_0 =="M" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.314,
Sex_0 =="M" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.609,
Sex_0 =="F" & Education4_0 == "Less than High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.803,
Sex_0 =="F" & Education4_0 == "High School Diploma" & MAT_Lang_0=="French" ~ MAT_Residual_0/7.079,
Sex_0 =="F" & Education4_0 == "Some College" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.451,
Sex_0 =="F" & Education4_0 == "College Degree or Higher" & MAT_Lang_0=="French" ~ MAT_Residual_0/6.734
),
MAT_Normed_0 = MAT_Z_0*3 +10,
MAT_Normed_0 = if_else(MAT_Normed_0 < 0, 0.01, MAT_Normed_0),
#Global Cognition Composite Score
Global_Composite_0 = RVLT_Immediate_Z_0 + RVLT_Delayed_Z_0 + Animal_Fluency_Z_0 + MAT_Z_0,
#FU1 Scores
#RVLT Immediate Score
RVLT_Immediate_Predicted_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 7.768 - 0.050*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 7.449 - 0.036*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ 10.095 - 0.073*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ 9.686 - 0.064*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 8.077 - 0.043*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ 9.806 - 0.059*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ 9.161 - 0.047*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ 9.804 - 0.053*Age_1,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 5.666 - 0.025*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 8.953 - 0.067*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ 7.662 - 0.039*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ 8.829 - 0.057*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 6.976 - 0.031*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ 8.667 - 0.045*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ 9.502 - 0.061*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ 9.013 - 0.047*Age_1
),
RVLT_Immediate_Residual_1 = RVLT_Immediate_Score_1 - RVLT_Immediate_Predicted_1,
RVLT_Immediate_Z_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.471,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.525,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.611,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.675,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.528,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.643,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.694,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="English" ~ RVLT_Immediate_Residual_1/1.802,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.290,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.473,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.913,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.641,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.623,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.595,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.605,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Immediate_Lang_1=="French" ~ RVLT_Immediate_Residual_1/1.715
),
RVLT_Immediate_Normed_1 = RVLT_Immediate_Z_1*3 + 10,
RVLT_Immediate_Normed_1 = if_else(RVLT_Immediate_Normed_1 < 0, 0.01, RVLT_Immediate_Normed_1),
#RVLT Delayed Score
RVLT_Delayed_Predicted_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 6.628 - 0.062*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 6.851 - 0.058*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ 8.289 - 0.076*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ 8.165 - 0.070*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 7.163 - 0.055*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ 8.115 - 0.063*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ 8.151 - 0.059*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ 8.844 - 0.066*Age_1,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 4.802 - 0.036*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 8.219 - 0.083*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ 9.721 - 0.097*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ 7.048 - 0.055*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 6.280 - 0.044*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ 6.999 - 0.042*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ 9.081 - 0.074*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ 8.712 - 0.066*Age_1
),
RVLT_Delayed_Residual_1 = RVLT_Delayed_Score_1 - RVLT_Delayed_Predicted_1,
RVLT_Delayed_Z_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.534,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.739,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.802,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.890,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.787,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/2.005,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/1.869,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="English" ~ RVLT_Delayed_Residual_1/2.135,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.559,
Sex_1 =="M" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.571,
Sex_1 =="M" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.815,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.721,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.859,
Sex_1 =="F" & Education4_1 == "High School Diploma" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.793,
Sex_1 =="F" & Education4_1 == "Some College" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.901,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & RVLT_Delayed_Lang_1=="French" ~ RVLT_Delayed_Residual_1/1.890
),
RVLT_Delayed_Normed_1 = RVLT_Delayed_Z_1*3 + 10,
RVLT_Delayed_Normed_1 = if_else(RVLT_Delayed_Normed_1 < 0, 0.01, RVLT_Delayed_Normed_1),
#Animal Fluency
Animal_Fluency_Predicted_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 23.132 - 0.095*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 28.923 - 0.157*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ 32.513 - 0.202*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ 31.143 - 0.168*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 23.433 - 0.114*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ 29.912 - 0.181*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ 30.764 - 0.178*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ 32.003 - 0.186*Age_1,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 26.034 - 0.152*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 33.358 - 0.241*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ 36.511 - 0.277*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ 30.193 - 0.179*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 21.460 - 0.089*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ 21.355 - 0.070*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ 30.881 - 0.205*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ 29.961 - 0.180*Age_1
),
Animal_Fluency_Residual_1 = Animal_Fluency_Strict_1 - Animal_Fluency_Predicted_1,
Animal_Fluency_Z_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.145,
Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.348,
Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.163,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.354,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/4.665,
Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/4.728,
Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.176,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="English" ~ Animal_Fluency_Residual_1/5.369,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/3.911,
Sex_1 =="M" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.889,
Sex_1 =="M" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/5.061,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.869,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.178,
Sex_1 =="F" & Education4_1 == "High School Diploma" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.321,
Sex_1 =="F" & Education4_1 == "Some College" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.468,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & Animal_Fluency_Lang_1=="French" ~ Animal_Fluency_Residual_1/4.940
),
Animal_Fluency_Normed_1 = Animal_Fluency_Z_1*3 + 10,
Animal_Fluency_Normed_1 = if_else(Animal_Fluency_Normed_1 < 0, 0.01, Animal_Fluency_Normed_1),
#Mental Alteration Test
MAT_Predicted_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ 33.295 - 0.161*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ 34.074 - 0.123*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ 41.488 - 0.219*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ 40.573 - 0.190*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ 39.102 - 0.251*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ 41.657 - 0.246*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ 36.877 - 0.168*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ 38.849 - 0.188*Age_1,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ 36.630 - 0.252*Age_1,
Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ 38.784 - 0.181*Age_1,
Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ 51.105 - 0.381*Age_1,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ 44.106 - 0.257*Age_1,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ 34.814 - 0.214*Age_1,
Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ 38.756 - 0.202*Age_1,
Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ 47.024 - 0.315*Age_1,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ 41.717 - 0.234*Age_1
),
MAT_Residual_1 = MAT_Score_1 - MAT_Predicted_1,
MAT_Z_1 = case_when(
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.602,
Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.702,
Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.490,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.727,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.080,
Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="English" ~ MAT_Residual_1/7.139,
Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="English" ~ MAT_Residual_1/6.915,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="English" ~ MAT_Residual_1/6.979,
Sex_1 =="M" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ MAT_Residual_1/7.589,
Sex_1 =="M" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ MAT_Residual_1/7.234,
Sex_1 =="M" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ MAT_Residual_1/6.314,
Sex_1 =="M" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ MAT_Residual_1/7.609,
Sex_1 =="F" & Education4_1 == "Less than High School Diploma" & MAT_Lang_1=="French" ~ MAT_Residual_1/6.803,
Sex_1 =="F" & Education4_1 == "High School Diploma" & MAT_Lang_1=="French" ~ MAT_Predicted_1/7.079,
Sex_1 =="F" & Education4_1 == "Some College" & MAT_Lang_1=="French" ~ MAT_Predicted_1/6.451,
Sex_1 =="F" & Education4_1 == "College Degree or Higher" & MAT_Lang_1=="French" ~ MAT_Predicted_1/6.734
),
MAT_Normed_1 = MAT_Z_1*3 + 10,
MAT_Normed_1 = if_else(MAT_Normed_1 < 0, 0.01, MAT_Normed_1),
#Global Cognition Composite Score
Global_Composite_1 = RVLT_Immediate_Z_1 + RVLT_Delayed_Z_1 + Animal_Fluency_Z_1 + MAT_Z_1,
#FU2 Scores
#RVLT Immediate Score
RVLT_Immediate_Predicted_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 7.768 - 0.050*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 7.449 - 0.036*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ 10.095 - 0.073*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ 9.686 - 0.064*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 8.077 - 0.043*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ 9.806 - 0.059*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ 9.161 - 0.047*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ 9.804 - 0.053*Age_2,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 5.666 - 0.025*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 8.953 - 0.067*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ 7.662 - 0.039*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ 8.829 - 0.057*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 6.976 - 0.031*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ 8.667 - 0.045*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ 9.502 - 0.061*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ 9.013 - 0.047*Age_2
),
RVLT_Immediate_Residual_2 = RVLT_Immediate_Score_2 - RVLT_Immediate_Predicted_2,
RVLT_Immediate_Z_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.471,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.525,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.611,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.675,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.528,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.643,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.694,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="English" ~ RVLT_Immediate_Residual_2/1.802,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.290,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.473,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.913,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.641,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.623,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.595,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.605,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Immediate_Lang_2=="French" ~ RVLT_Immediate_Residual_2/1.715
),
RVLT_Immediate_Normed_2 = RVLT_Immediate_Z_2*3 + 10,
RVLT_Immediate_Normed_2 = if_else(RVLT_Immediate_Normed_2 < 0, 0.01, RVLT_Immediate_Normed_2),
#RVLT Delayed Score
RVLT_Delayed_Predicted_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 6.628 - 0.062*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 6.851 - 0.058*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ 8.289 - 0.076*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ 8.165 - 0.070*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 7.163 - 0.055*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ 8.115 - 0.063*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ 8.151 - 0.059*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ 8.844 - 0.066*Age_2,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 4.802 - 0.036*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 8.219 - 0.083*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ 9.721 - 0.097*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ 7.048 - 0.055*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 6.280 - 0.044*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ 6.999 - 0.042*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ 9.081 - 0.074*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ 8.712 - 0.066*Age_2
),
RVLT_Delayed_Residual_2 = RVLT_Delayed_Score_2 - RVLT_Delayed_Predicted_2,
RVLT_Delayed_Z_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.534,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.739,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.802,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.890,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.787,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/2.005,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/1.869,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="English" ~ RVLT_Delayed_Residual_2/2.135,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.559,
Sex_2 =="M" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.571,
Sex_2 =="M" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.815,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.721,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.859,
Sex_2 =="F" & Education4_2 == "High School Diploma" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.793,
Sex_2 =="F" & Education4_2 == "Some College" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.901,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & RVLT_Delayed_Lang_2=="French" ~ RVLT_Delayed_Residual_2/1.890
),
RVLT_Delayed_Normed_2 = RVLT_Delayed_Z_2*3 + 10,
RVLT_Delayed_Normed_2 = if_else(RVLT_Delayed_Normed_2 < 0, 0.01, RVLT_Delayed_Normed_2),
#Animal Fluency
Animal_Fluency_Predicted_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 23.132 - 0.095*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 28.923 - 0.157*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ 32.513 - 0.202*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ 31.143 - 0.168*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 23.433 - 0.114*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ 29.912 - 0.181*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ 30.764 - 0.178*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ 32.003 - 0.186*Age_2,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 26.034 - 0.152*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 33.358 - 0.241*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ 36.511 - 0.277*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ 30.193 - 0.179*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 21.460 - 0.089*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ 21.355 - 0.070*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ 30.881 - 0.205*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ 29.961 - 0.180*Age_2
),
Animal_Fluency_Residual_2 = Animal_Fluency_Strict_2 - Animal_Fluency_Predicted_2,
Animal_Fluency_Z_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.145,
Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.348,
Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.163,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.354,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/4.665,
Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/4.728,
Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.176,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="English" ~ Animal_Fluency_Residual_2/5.369,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/3.911,
Sex_2 =="M" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.889,
Sex_2 =="M" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/5.061,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.869,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.178,
Sex_2 =="F" & Education4_2 == "High School Diploma" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.321,
Sex_2 =="F" & Education4_2 == "Some College" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.468,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & Animal_Fluency_Lang_2=="French" ~ Animal_Fluency_Residual_2/4.940
),
Animal_Fluency_Normed_2 = Animal_Fluency_Z_2*3 + 10,
Animal_Fluency_Normed_2 = if_else(Animal_Fluency_Normed_2 < 0, 0.01, Animal_Fluency_Normed_2),
#Mental Alteration Test
MAT_Predicted_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ 33.295 - 0.161*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ 34.074 - 0.123*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ 41.488 - 0.219*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ 40.573 - 0.190*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ 39.102 - 0.251*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ 41.657 - 0.246*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ 36.877 - 0.168*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ 38.849 - 0.188*Age_2,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ 36.630 - 0.252*Age_2,
Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ 38.784 - 0.181*Age_2,
Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ 51.105 - 0.381*Age_2,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ 44.106 - 0.257*Age_2,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ 34.814 - 0.214*Age_2,
Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ 38.756 - 0.202*Age_2,
Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ 47.024 - 0.315*Age_2,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ 41.717 - 0.234*Age_2
),
MAT_Residual_2 = MAT_Score_2 - MAT_Predicted_2,
MAT_Z_2 = case_when(
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.602,
Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.702,
Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.490,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.727,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.080,
Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="English" ~ MAT_Residual_2/7.139,
Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="English" ~ MAT_Residual_2/6.915,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="English" ~ MAT_Residual_2/6.979,
Sex_2 =="M" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.589,
Sex_2 =="M" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.234,
Sex_2 =="M" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.314,
Sex_2 =="M" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.609,
Sex_2 =="F" & Education4_2 == "Less than High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.803,
Sex_2 =="F" & Education4_2 == "High School Diploma" & MAT_Lang_2=="French" ~ MAT_Residual_2/7.079,
Sex_2 =="F" & Education4_2 == "Some College" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.451,
Sex_2 =="F" & Education4_2 == "College Degree or Higher" & MAT_Lang_2=="French" ~ MAT_Residual_2/6.734
),
MAT_Normed_2 = MAT_Z_2*3 + 10,
MAT_Normed_2 = if_else(MAT_Normed_2 < 0, 0.01, MAT_Normed_2),
#Global Cognition Composite Score
Global_Composite_2 = RVLT_Immediate_Z_2 + RVLT_Delayed_Z_2 + Animal_Fluency_Z_0 + MAT_Z_2
)
Finally, we categorized participants as having their follow-up 2 data collected before or after the start of the COVID-19 pandemic.
Tracking.Adjusted_Final <- Tracking.Adjusted_Full %>%
mutate(timestamp = ymd_hms(startdate_TRF2_2, tz = "EST"))
## Date in ISO8601 format; converting timezone from UTC to "EST".
start_time <- as.POSIXct("2020-03-11 00:00:00", tz = "EST")
Tracking.Adjusted_Final$Pandemic<-NA
Tracking.Adjusted_Final$Pandemic[Tracking.Adjusted_Final$timestamp>=start_time]<-"FU2 data collected after COVID-19"
Tracking.Adjusted_Final$Pandemic[Tracking.Adjusted_Final$timestamp<start_time]<-"FU2 data collected before COVID-19"
Full tracking cohort at baseline (N=21,241)
Track.Baseline.total <- Track.Baseline.Final %>%
count()
print(Track.Baseline.total)
## n
## 1 21241
Excluding tracking cohort participants lost to follow-up (N=14,697)
Track.Complete <- Track.Full %>%
count()
print(Track.Complete)
## n
## 1 14697
Baseline tracking cohort removing individuals who reported SCI or dementia at baseline, FU1, or FU2 (N=13,934)
Track.SCI_Dementia <- Track.Full %>%
group_by(SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2) %>%
count()
print(Track.SCI_Dementia)
## # A tibble: 40 × 7
## # Groups: SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2 [40]
## SCI_0 Dementia_0 SCI_1 Dementia_1 SCI_2 Dementia_2 n
## <chr> <chr> <chr> <chr> <chr> <chr> <int>
## 1 No No No No No No 13934
## 2 No No No No No Yes 7
## 3 No No No No No <NA> 7
## 4 No No No No Yes No 143
## 5 No No No No Yes Yes 18
## 6 No No No No Yes <NA> 2
## 7 No No No No <NA> No 15
## 8 No No No No <NA> Yes 1
## 9 No No No No <NA> <NA> 160
## 10 No No No Yes No <NA> 3
## # ℹ 30 more rows
Baseline tracking cohort removing individuals with dementia or SCI and without full cog data (N=11,355)
Track.FullCogs <- Track.Full %>%
group_by(SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2,
!is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0), !is.na(RVLT_Delayed_Score_0),!is.na(Animal_Fluency_Strict_0)) %>%
count()
print(Track.FullCogs)
## # A tibble: 105 × 11
## # Groups: SCI_0, Dementia_0, SCI_1, Dementia_1, SCI_2, Dementia_2,
## # !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0),
## # !is.na(RVLT_Delayed_Score_0), !is.na(Animal_Fluency_Strict_0) [105]
## SCI_0 Dementia_0 SCI_1 Dementia_1 SCI_2 Dementia_2 `!is.na(MAT_Score_0)`
## <chr> <chr> <chr> <chr> <chr> <chr> <lgl>
## 1 No No No No No No FALSE
## 2 No No No No No No FALSE
## 3 No No No No No No FALSE
## 4 No No No No No No FALSE
## 5 No No No No No No FALSE
## 6 No No No No No No FALSE
## 7 No No No No No No FALSE
## 8 No No No No No No FALSE
## 9 No No No No No No TRUE
## 10 No No No No No No TRUE
## # ℹ 95 more rows
## # ℹ 4 more variables: `!is.na(RVLT_Immediate_Score_0)` <lgl>,
## # `!is.na(RVLT_Delayed_Score_0)` <lgl>,
## # `!is.na(Animal_Fluency_Strict_0)` <lgl>, n <int>
Final sample size (N=11,355) with complete cognitive data at FU1 grouped by Pandemic cohort
Track.FullCogs2 <- Tracking.Adjusted_Final %>%
group_by(!is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0), !is.na(RVLT_Delayed_Score_0),!is.na(Animal_Fluency_Strict_0),
!is.na(MAT_Score_1), !is.na(RVLT_Immediate_Score_1), !is.na(RVLT_Delayed_Score_1),!is.na(Animal_Fluency_Strict_1), Pandemic) %>%
count()
print(Track.FullCogs2)
## # A tibble: 29 × 10
## # Groups: !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0),
## # !is.na(RVLT_Delayed_Score_0), !is.na(Animal_Fluency_Strict_0),
## # !is.na(MAT_Score_1), !is.na(RVLT_Immediate_Score_1),
## # !is.na(RVLT_Delayed_Score_1), !is.na(Animal_Fluency_Strict_1), Pandemic
## # [29]
## `!is.na(MAT_Score_0)` `!is.na(RVLT_Immediate_Score_0)` !is.na(RVLT_Delayed_…¹
## <lgl> <lgl> <lgl>
## 1 TRUE TRUE TRUE
## 2 TRUE TRUE TRUE
## 3 TRUE TRUE TRUE
## 4 TRUE TRUE TRUE
## 5 TRUE TRUE TRUE
## 6 TRUE TRUE TRUE
## 7 TRUE TRUE TRUE
## 8 TRUE TRUE TRUE
## 9 TRUE TRUE TRUE
## 10 TRUE TRUE TRUE
## # ℹ 19 more rows
## # ℹ abbreviated name: ¹`!is.na(RVLT_Delayed_Score_0)`
## # ℹ 7 more variables: `!is.na(Animal_Fluency_Strict_0)` <lgl>,
## # `!is.na(MAT_Score_1)` <lgl>, `!is.na(RVLT_Immediate_Score_1)` <lgl>,
## # `!is.na(RVLT_Delayed_Score_1)` <lgl>,
## # `!is.na(Animal_Fluency_Strict_1)` <lgl>, Pandemic <chr>, n <int>
Final sample size (N=11,355) with complete cognitive data at FU2 grouped by Pandemic cohort
Track.FullCogs3 <- Tracking.Adjusted_Final %>%
group_by(!is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0), !is.na(RVLT_Delayed_Score_0),!is.na(Animal_Fluency_Strict_0),
!is.na(MAT_Score_2), !is.na(RVLT_Immediate_Score_2), !is.na(RVLT_Delayed_Score_2),!is.na(Animal_Fluency_Strict_2), Pandemic) %>%
count()
print(Track.FullCogs3)
## # A tibble: 25 × 10
## # Groups: !is.na(MAT_Score_0), !is.na(RVLT_Immediate_Score_0),
## # !is.na(RVLT_Delayed_Score_0), !is.na(Animal_Fluency_Strict_0),
## # !is.na(MAT_Score_2), !is.na(RVLT_Immediate_Score_2),
## # !is.na(RVLT_Delayed_Score_2), !is.na(Animal_Fluency_Strict_2), Pandemic
## # [25]
## `!is.na(MAT_Score_0)` `!is.na(RVLT_Immediate_Score_0)` !is.na(RVLT_Delayed_…¹
## <lgl> <lgl> <lgl>
## 1 TRUE TRUE TRUE
## 2 TRUE TRUE TRUE
## 3 TRUE TRUE TRUE
## 4 TRUE TRUE TRUE
## 5 TRUE TRUE TRUE
## 6 TRUE TRUE TRUE
## 7 TRUE TRUE TRUE
## 8 TRUE TRUE TRUE
## 9 TRUE TRUE TRUE
## 10 TRUE TRUE TRUE
## # ℹ 15 more rows
## # ℹ abbreviated name: ¹`!is.na(RVLT_Delayed_Score_0)`
## # ℹ 7 more variables: `!is.na(Animal_Fluency_Strict_0)` <lgl>,
## # `!is.na(MAT_Score_2)` <lgl>, `!is.na(RVLT_Immediate_Score_2)` <lgl>,
## # `!is.na(RVLT_Delayed_Score_2)` <lgl>,
## # `!is.na(Animal_Fluency_Strict_2)` <lgl>, Pandemic <chr>, n <int>
Final sample size (N=11,355) and number of participants with PASE score at baseline (N=9,181)
```r
PASEBL <- Tracking.Adjusted_Final %>%
group_by(!is.na(PASE_TOTAL_0), Pandemic) %>%
count()
print(PASEBL)
## # A tibble: 4 × 3
## # Groups: !is.na(PASE_TOTAL_0), Pandemic [4]
## `!is.na(PASE_TOTAL_0)` Pandemic n
## <lgl> <chr> <int>
## 1 FALSE FU2 data collected after COVID-19 1027
## 2 FALSE FU2 data collected before COVID-19 1147
## 3 TRUE FU2 data collected after COVID-19 4154
## 4 TRUE FU2 data collected before COVID-19 5027
Final sample size (N=11,355) and number of participants with PASE score at baseline or FU1 (N=1,945)
PASEBL <- Tracking.Adjusted_Final %>%
group_by(!is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1),Pandemic) %>%
count()
print(PASEBL)
## # A tibble: 8 × 4
## # Groups: !is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1), Pandemic [8]
## `!is.na(PASE_TOTAL_0)` `!is.na(PASE_TOTAL_1)` Pandemic n
## <lgl> <lgl> <chr> <int>
## 1 FALSE FALSE FU2 data collected after … 882
## 2 FALSE FALSE FU2 data collected before… 1016
## 3 FALSE TRUE FU2 data collected after … 145
## 4 FALSE TRUE FU2 data collected before… 131
## 5 TRUE FALSE FU2 data collected after … 3226
## 6 TRUE FALSE FU2 data collected before… 4010
## 7 TRUE TRUE FU2 data collected after … 928
## 8 TRUE TRUE FU2 data collected before… 1017
Final sample size (N=11,355) and number of participants with PASE score at baseline, FU1, or FU2 (N=762)
PASEBL <- Tracking.Adjusted_Final %>%
group_by(!is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1), , !is.na(PASE_TOTAL_2),Pandemic) %>%
count()
print(PASEBL)
## # A tibble: 16 × 5
## # Groups: !is.na(PASE_TOTAL_0), !is.na(PASE_TOTAL_1), !is.na(PASE_TOTAL_2),
## # Pandemic [16]
## `!is.na(PASE_TOTAL_0)` `!is.na(PASE_TOTAL_1)` `!is.na(PASE_TOTAL_2)` Pandemic
## <lgl> <lgl> <lgl> <chr>
## 1 FALSE FALSE FALSE FU2 dat…
## 2 FALSE FALSE FALSE FU2 dat…
## 3 FALSE FALSE TRUE FU2 dat…
## 4 FALSE FALSE TRUE FU2 dat…
## 5 FALSE TRUE FALSE FU2 dat…
## 6 FALSE TRUE FALSE FU2 dat…
## 7 FALSE TRUE TRUE FU2 dat…
## 8 FALSE TRUE TRUE FU2 dat…
## 9 TRUE FALSE FALSE FU2 dat…
## 10 TRUE FALSE FALSE FU2 dat…
## 11 TRUE FALSE TRUE FU2 dat…
## 12 TRUE FALSE TRUE FU2 dat…
## 13 TRUE TRUE FALSE FU2 dat…
## 14 TRUE TRUE FALSE FU2 dat…
## 15 TRUE TRUE TRUE FU2 dat…
## 16 TRUE TRUE TRUE FU2 dat…
## # ℹ 1 more variable: n <int>
Final sample size (N=11,355) and number of participants with sleep data at baseline (N=11,334)
PASEBL <- Tracking.Adjusted_Final %>%
group_by(!is.na(RSTLS_Sleep_0),Pandemic) %>%
count()
print(PASEBL)
## # A tibble: 4 × 3
## # Groups: !is.na(RSTLS_Sleep_0), Pandemic [4]
## `!is.na(RSTLS_Sleep_0)` Pandemic n
## <lgl> <chr> <int>
## 1 FALSE FU2 data collected after COVID-19 11
## 2 FALSE FU2 data collected before COVID-19 10
## 3 TRUE FU2 data collected after COVID-19 5170
## 4 TRUE FU2 data collected before COVID-19 6164
Final sample size (N=11,355) and number of participants with sleep data at baseline or FU1 (N=11,304)
PASEBL <- Tracking.Adjusted_Final %>%
group_by(!is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1),Pandemic) %>%
count()
print(PASEBL)
## # A tibble: 6 × 4
## # Groups: !is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1), Pandemic [6]
## `!is.na(RSTLS_Sleep_0)` `!is.na(RSTLS_Sleep_1)` Pandemic n
## <lgl> <lgl> <chr> <int>
## 1 FALSE TRUE FU2 data collected afte… 11
## 2 FALSE TRUE FU2 data collected befo… 10
## 3 TRUE FALSE FU2 data collected afte… 7
## 4 TRUE FALSE FU2 data collected befo… 23
## 5 TRUE TRUE FU2 data collected afte… 5163
## 6 TRUE TRUE FU2 data collected befo… 6141
Final sample size (N=11,355) and number of participants with sleep data at baseline, FU1, or FU2 (N=11,270)
PASEBL <- Tracking.Adjusted_Final %>%
group_by(!is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1), , !is.na(RSTLS_Sleep_2),Pandemic) %>%
count()
print(PASEBL)
## # A tibble: 8 × 5
## # Groups: !is.na(RSTLS_Sleep_0), !is.na(RSTLS_Sleep_1),
## # !is.na(RSTLS_Sleep_2), Pandemic [8]
## `!is.na(RSTLS_Sleep_0)` !is.na(RSTLS_Sleep_1…¹ !is.na(RSTLS_Sleep_2…² Pandemic
## <lgl> <lgl> <lgl> <chr>
## 1 FALSE TRUE TRUE FU2 dat…
## 2 FALSE TRUE TRUE FU2 dat…
## 3 TRUE FALSE TRUE FU2 dat…
## 4 TRUE FALSE TRUE FU2 dat…
## 5 TRUE TRUE FALSE FU2 dat…
## 6 TRUE TRUE FALSE FU2 dat…
## 7 TRUE TRUE TRUE FU2 dat…
## 8 TRUE TRUE TRUE FU2 dat…
## # ℹ abbreviated names: ¹`!is.na(RSTLS_Sleep_1)`, ²`!is.na(RSTLS_Sleep_2)`
## # ℹ 1 more variable: n <int>
Create factor variables for PASE sedentary behaviour and sleep score
BL.data<-Tracking.Adjusted_Final
BL.data$PASE_Q1B_0 <- as.factor(ifelse(BL.data$PASE_Q1B_0==10, 1, 0))
BL.data$RSTLS_Sleep_0 <- as.factor(BL.data$RSTLS_Sleep_0)
Final baseline sample (N= 11,355)
Baseline<-dput(names(BL.data[c(5,4,14,12,6,7,8,9,10,11,15,18,19,21,98,102,106,110,29,31,27,24,13,22,23)]))
## c("Age_0", "Sex_0", "BMI_0", "Ethnicity_0", "Relationship_status_0",
## "Education4_0", "Income_Level_0", "Living_status_0", "Alcohol_0",
## "Smoking_Status_0", "CESD_10_0", "Anxiety_0", "Mood_Disord_0",
## "Chronic_conditions_0", "RVLT_Immediate_Normed_0", "RVLT_Delayed_Normed_0",
## "Animal_Fluency_Normed_0", "MAT_Normed_0", "RVLT_Immediate_Lang_0",
## "RVLT_Delayed_Lang_0", "MAT_Lang_0", "Animal_Fluency_Lang_0",
## "PASE_TOTAL_0", "PASE_Q1B_0", "RSTLS_Sleep_0")
Table1_Final<-CreateTableOne(vars=Baseline, data=BL.data)
print(Table1_Final,contDigits=2,missing=TRUE,quote=TRUE)
## ""
## "" "Overall" "Missing"
## "n" " 11355" " "
## "Age_0 (mean (SD))" " 61.62 (10.08)" " 0.0"
## "Sex_0 = M (%)" " 5467 (48.1) " " 0.0"
## "BMI_0 (mean (SD))" " 27.50 (5.10)" " 0.5"
## "Ethnicity_0 = White (%)" " 11043 (97.3) " " 0.0"
## "Relationship_status_0 (%)" " " " 0.0"
## " Divorced" " 997 ( 8.8) " " "
## " Married" " 8210 (72.3) " " "
## " Separated" " 290 ( 2.6) " " "
## " Single" " 852 ( 7.5) " " "
## " Widowed" " 1001 ( 8.8) " " "
## "Education4_0 (%)" " " " 0.0"
## " College Degree or Higher" " 8322 (73.3) " " "
## " High School Diploma" " 1449 (12.8) " " "
## " Less than High School Diploma" " 749 ( 6.6) " " "
## " Some College" " 835 ( 7.4) " " "
## "Income_Level_0 (%)" " " " 3.5"
## " <$20k" " 1764 (16.1) " " "
## " >$150k" " 428 ( 3.9) " " "
## " $100-150k" " 836 ( 7.6) " " "
## " $20-50k" " 4349 (39.7) " " "
## " $50-100k" " 3584 (32.7) " " "
## "Living_status_0 (%)" " " " 0.0"
## " Apartment/Condo/Townhome" " 1342 (11.8) " " "
## " Assisted Living" " 61 ( 0.5) " " "
## " House" " 9852 (86.8) " " "
## " Other" " 100 ( 0.9) " " "
## "Alcohol_0 (%)" " " " 3.1"
## " Non-drinker" " 1154 (10.5) " " "
## " Occasional drinker" " 1729 (15.7) " " "
## " Regular drinker (at least once a month)" " 8124 (73.8) " " "
## "Smoking_Status_0 (%)" " " " 0.4"
## " Daily Smoker" " 744 ( 6.6) " " "
## " Former Smoker" " 6830 (60.4) " " "
## " Never Smoked" " 3541 (31.3) " " "
## " Occasional Smoker" " 189 ( 1.7) " " "
## "CESD_10_0 (mean (SD))" " 4.96 (4.36)" " 0.3"
## "Anxiety_0 = Yes (%)" " 718 ( 6.3) " " 0.1"
## "Mood_Disord_0 = Yes (%)" " 1508 (13.3) " " 0.1"
## "Chronic_conditions_0 (mean (SD))" " 2.77 (2.26)" " 3.8"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.44 (3.81)" " 0.0"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.71 (3.71)" " 0.0"
## "Animal_Fluency_Normed_0 (mean (SD))" " 10.29 (3.08)" " 0.0"
## "MAT_Normed_0 (mean (SD))" " 9.95 (3.46)" " 0.0"
## "RVLT_Immediate_Lang_0 = French (%)" " 1962 (17.3) " " 0.0"
## "RVLT_Delayed_Lang_0 = French (%)" " 1962 (17.3) " " 0.0"
## "MAT_Lang_0 = French (%)" " 1962 (17.3) " " 0.0"
## "Animal_Fluency_Lang_0 = French (%)" " 1962 (17.3) " " 0.0"
## "PASE_TOTAL_0 (mean (SD))" "168.93 (78.96)" "19.1"
## "PASE_Q1B_0 = 1 (%)" " 4305 (38.6) " " 1.8"
## "RSTLS_Sleep_0 = 1 (%)" " 3741 (33.0) " " 0.2"
Final baseline sample stratified by whether FU2 data was collected before (N= 6,174) or after (N= 5,181) the start of the COVID-19 pandemic
Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.data)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
## "Stratified by Pandemic"
## "" "FU2 data collected after COVID-19"
## "n" " 5181"
## "Age_0 (mean (SD))" " 60.29 (10.54)"
## "Sex_0 = M (%)" " 2856 (55.1) "
## "BMI_0 (mean (SD))" " 27.52 (4.94)"
## "Ethnicity_0 = White (%)" " 5025 (97.0) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 408 ( 7.9) "
## " Married" " 3841 (74.2) "
## " Separated" " 150 ( 2.9) "
## " Single" " 383 ( 7.4) "
## " Widowed" " 397 ( 7.7) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 3605 (69.6) "
## " High School Diploma" " 776 (15.0) "
## " Less than High School Diploma" " 411 ( 7.9) "
## " Some College" " 389 ( 7.5) "
## "Income_Level_0 (%)" " "
## " <$20k" " 783 (15.6) "
## " >$150k" " 239 ( 4.8) "
## " $100-150k" " 426 ( 8.5) "
## " $20-50k" " 1874 (37.3) "
## " $50-100k" " 1700 (33.9) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 579 (11.2) "
## " Assisted Living" " 24 ( 0.5) "
## " House" " 4541 (87.6) "
## " Other" " 37 ( 0.7) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 512 (10.2) "
## " Occasional drinker" " 784 (15.6) "
## " Regular drinker (at least once a month)" " 3731 (74.2) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 361 ( 7.0) "
## " Former Smoker" " 3128 (60.6) "
## " Never Smoked" " 1579 (30.6) "
## " Occasional Smoker" " 90 ( 1.7) "
## "CESD_10_0 (mean (SD))" " 5.08 (4.47)"
## "Anxiety_0 = Yes (%)" " 335 ( 6.5) "
## "Mood_Disord_0 = Yes (%)" " 698 (13.5) "
## "Chronic_conditions_0 (mean (SD))" " 2.60 (2.22)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.13 (3.76)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.45 (3.69)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 10.19 (3.09)"
## "MAT_Normed_0 (mean (SD))" " 9.82 (3.46)"
## "RVLT_Immediate_Lang_0 = French (%)" " 1319 (25.5) "
## "RVLT_Delayed_Lang_0 = French (%)" " 1319 (25.5) "
## "MAT_Lang_0 = French (%)" " 1319 (25.5) "
## "Animal_Fluency_Lang_0 = French (%)" " 1319 (25.5) "
## "PASE_TOTAL_0 (mean (SD))" "179.56 (81.39)"
## "PASE_Q1B_0 = 1 (%)" " 1875 (37.2) "
## "RSTLS_Sleep_0 = 1 (%)" " 1710 (33.1) "
## "Stratified by Pandemic"
## "" "FU2 data collected before COVID-19"
## "n" " 6174"
## "Age_0 (mean (SD))" " 62.74 (9.53)"
## "Sex_0 = M (%)" " 2611 (42.3) "
## "BMI_0 (mean (SD))" " 27.48 (5.24)"
## "Ethnicity_0 = White (%)" " 6018 (97.5) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 589 ( 9.5) "
## " Married" " 4369 (70.8) "
## " Separated" " 140 ( 2.3) "
## " Single" " 469 ( 7.6) "
## " Widowed" " 604 ( 9.8) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 4717 (76.4) "
## " High School Diploma" " 673 (10.9) "
## " Less than High School Diploma" " 338 ( 5.5) "
## " Some College" " 446 ( 7.2) "
## "Income_Level_0 (%)" " "
## " <$20k" " 981 (16.5) "
## " >$150k" " 189 ( 3.2) "
## " $100-150k" " 410 ( 6.9) "
## " $20-50k" " 2475 (41.7) "
## " $50-100k" " 1884 (31.7) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 763 (12.4) "
## " Assisted Living" " 37 ( 0.6) "
## " House" " 5311 (86.0) "
## " Other" " 63 ( 1.0) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 642 (10.7) "
## " Occasional drinker" " 945 (15.8) "
## " Regular drinker (at least once a month)" " 4393 (73.5) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 383 ( 6.2) "
## " Former Smoker" " 3702 (60.2) "
## " Never Smoked" " 1962 (31.9) "
## " Occasional Smoker" " 99 ( 1.6) "
## "CESD_10_0 (mean (SD))" " 4.85 (4.27)"
## "Anxiety_0 = Yes (%)" " 383 ( 6.2) "
## "Mood_Disord_0 = Yes (%)" " 810 (13.1) "
## "Chronic_conditions_0 (mean (SD))" " 2.91 (2.29)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.70 (3.84)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.93 (3.72)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 10.38 (3.06)"
## "MAT_Normed_0 (mean (SD))" " 10.06 (3.46)"
## "RVLT_Immediate_Lang_0 = French (%)" " 643 (10.4) "
## "RVLT_Delayed_Lang_0 = French (%)" " 643 (10.4) "
## "MAT_Lang_0 = French (%)" " 643 (10.4) "
## "Animal_Fluency_Lang_0 = French (%)" " 643 (10.4) "
## "PASE_TOTAL_0 (mean (SD))" "160.16 (75.79)"
## "PASE_Q1B_0 = 1 (%)" " 2430 (39.7) "
## "RSTLS_Sleep_0 = 1 (%)" " 2031 (32.9) "
## "Stratified by Pandemic"
## "" "p" "test" "Missing"
## "n" "" "" " "
## "Age_0 (mean (SD))" "<0.001" "" " 0.0"
## "Sex_0 = M (%)" "<0.001" "" " 0.0"
## "BMI_0 (mean (SD))" " 0.688" "" " 0.5"
## "Ethnicity_0 = White (%)" " 0.130" "" " 0.0"
## "Relationship_status_0 (%)" "<0.001" "" " 0.0"
## " Divorced" "" "" " "
## " Married" "" "" " "
## " Separated" "" "" " "
## " Single" "" "" " "
## " Widowed" "" "" " "
## "Education4_0 (%)" "<0.001" "" " 0.0"
## " College Degree or Higher" "" "" " "
## " High School Diploma" "" "" " "
## " Less than High School Diploma" "" "" " "
## " Some College" "" "" " "
## "Income_Level_0 (%)" "<0.001" "" " 3.5"
## " <$20k" "" "" " "
## " >$150k" "" "" " "
## " $100-150k" "" "" " "
## " $20-50k" "" "" " "
## " $50-100k" "" "" " "
## "Living_status_0 (%)" " 0.043" "" " 0.0"
## " Apartment/Condo/Townhome" "" "" " "
## " Assisted Living" "" "" " "
## " House" "" "" " "
## " Other" "" "" " "
## "Alcohol_0 (%)" " 0.584" "" " 3.1"
## " Non-drinker" "" "" " "
## " Occasional drinker" "" "" " "
## " Regular drinker (at least once a month)" "" "" " "
## "Smoking_Status_0 (%)" " 0.219" "" " 0.4"
## " Daily Smoker" "" "" " "
## " Former Smoker" "" "" " "
## " Never Smoked" "" "" " "
## " Occasional Smoker" "" "" " "
## "CESD_10_0 (mean (SD))" " 0.005" "" " 0.3"
## "Anxiety_0 = Yes (%)" " 0.589" "" " 0.1"
## "Mood_Disord_0 = Yes (%)" " 0.604" "" " 0.1"
## "Chronic_conditions_0 (mean (SD))" "<0.001" "" " 3.8"
## "RVLT_Immediate_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "RVLT_Delayed_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "Animal_Fluency_Normed_0 (mean (SD))" " 0.001" "" " 0.0"
## "MAT_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "RVLT_Immediate_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "RVLT_Delayed_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "MAT_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "Animal_Fluency_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "PASE_TOTAL_0 (mean (SD))" "<0.001" "" "19.1"
## "PASE_Q1B_0 = 1 (%)" " 0.008" "" " 1.8"
## "RSTLS_Sleep_0 = 1 (%)" " 0.903" "" " 0.2"
Stratify Results By Age, Sex, and Pandemic Status
BL.data$Age_sex<-NA
BL.data$Age_sex[BL.data$Age_0<=64 & BL.data$Sex_0 == "M"]<-"Males 45-64"
BL.data$Age_sex[BL.data$Age_0<=64 & BL.data$Sex_0 == "F"]<-"Females 45-64"
BL.data$Age_sex[BL.data$Age_0>64 & BL.data$Sex_0 == "M"]<-"Males 65+"
BL.data$Age_sex[BL.data$Age_0>64 & BL.data$Sex_0 == "F"]<-"Females 65+"
BL.MalesYoung<-subset(BL.data, Age_sex=="Males 45-64")
BL.FemalesYoung<-subset(BL.data, Age_sex=="Females 45-64")
BL.MalesOld<-subset(BL.data, Age_sex=="Males 65+")
BL.FemalesOld<-subset(BL.data, Age_sex=="Females 65+")
Males 45-64
Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.MalesYoung)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
## "Stratified by Pandemic"
## "" "FU2 data collected after COVID-19"
## "n" " 2032"
## "Age_0 (mean (SD))" " 54.12 (5.36)"
## "Sex_0 = M (%)" " 2032 (100.0) "
## "BMI_0 (mean (SD))" " 27.98 (4.44)"
## "Ethnicity_0 = White (%)" " 1947 ( 95.8) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 126 ( 6.2) "
## " Married" " 1656 ( 81.5) "
## " Separated" " 64 ( 3.2) "
## " Single" " 163 ( 8.0) "
## " Widowed" " 22 ( 1.1) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 1555 ( 76.5) "
## " High School Diploma" " 248 ( 12.2) "
## " Less than High School Diploma" " 95 ( 4.7) "
## " Some College" " 134 ( 6.6) "
## "Income_Level_0 (%)" " "
## " <$20k" " 123 ( 6.2) "
## " >$150k" " 188 ( 9.4) "
## " $100-150k" " 297 ( 14.9) "
## " $20-50k" " 517 ( 25.9) "
## " $50-100k" " 873 ( 43.7) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 146 ( 7.2) "
## " Assisted Living" " 2 ( 0.1) "
## " House" " 1872 ( 92.1) "
## " Other" " 12 ( 0.6) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 174 ( 8.7) "
## " Occasional drinker" " 223 ( 11.2) "
## " Regular drinker (at least once a month)" " 1597 ( 80.1) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 168 ( 8.3) "
## " Former Smoker" " 1211 ( 59.8) "
## " Never Smoked" " 605 ( 29.9) "
## " Occasional Smoker" " 40 ( 2.0) "
## "CESD_10_0 (mean (SD))" " 4.82 (4.40)"
## "Anxiety_0 = Yes (%)" " 100 ( 4.9) "
## "Mood_Disord_0 = Yes (%)" " 257 ( 12.7) "
## "Chronic_conditions_0 (mean (SD))" " 1.91 (1.76)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 9.73 (3.52)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.02 (3.56)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 10.17 (3.15)"
## "MAT_Normed_0 (mean (SD))" " 9.75 (3.41)"
## "RVLT_Immediate_Lang_0 = French (%)" " 485 ( 23.9) "
## "RVLT_Delayed_Lang_0 = French (%)" " 485 ( 23.9) "
## "MAT_Lang_0 = French (%)" " 485 ( 23.9) "
## "Animal_Fluency_Lang_0 = French (%)" " 485 ( 23.9) "
## "PASE_TOTAL_0 (mean (SD))" "217.41 (81.01)"
## "PASE_Q1B_0 = 1 (%)" " 630 ( 31.9) "
## "RSTLS_Sleep_0 = 1 (%)" " 634 ( 31.2) "
## "Stratified by Pandemic"
## "" "FU2 data collected before COVID-19"
## "n" " 1424"
## "Age_0 (mean (SD))" " 56.68 (5.24)"
## "Sex_0 = M (%)" " 1424 (100.0) "
## "BMI_0 (mean (SD))" " 28.10 (4.65)"
## "Ethnicity_0 = White (%)" " 1381 ( 97.0) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 73 ( 5.1) "
## " Married" " 1152 ( 81.0) "
## " Separated" " 43 ( 3.0) "
## " Single" " 127 ( 8.9) "
## " Widowed" " 28 ( 2.0) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 1125 ( 79.0) "
## " High School Diploma" " 147 ( 10.3) "
## " Less than High School Diploma" " 50 ( 3.5) "
## " Some College" " 102 ( 7.2) "
## "Income_Level_0 (%)" " "
## " <$20k" " 94 ( 6.8) "
## " >$150k" " 92 ( 6.6) "
## " $100-150k" " 207 ( 14.9) "
## " $20-50k" " 414 ( 29.7) "
## " $50-100k" " 585 ( 42.0) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 122 ( 8.6) "
## " Assisted Living" " 1 ( 0.1) "
## " House" " 1290 ( 90.6) "
## " Other" " 11 ( 0.8) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 131 ( 9.3) "
## " Occasional drinker" " 136 ( 9.7) "
## " Regular drinker (at least once a month)" " 1135 ( 81.0) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 105 ( 7.4) "
## " Former Smoker" " 874 ( 61.7) "
## " Never Smoked" " 401 ( 28.3) "
## " Occasional Smoker" " 37 ( 2.6) "
## "CESD_10_0 (mean (SD))" " 4.51 (4.01)"
## "Anxiety_0 = Yes (%)" " 68 ( 4.8) "
## "Mood_Disord_0 = Yes (%)" " 144 ( 10.1) "
## "Chronic_conditions_0 (mean (SD))" " 2.11 (1.75)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.40 (3.86)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.60 (3.77)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 10.32 (3.29)"
## "MAT_Normed_0 (mean (SD))" " 10.02 (3.38)"
## "RVLT_Immediate_Lang_0 = French (%)" " 147 ( 10.3) "
## "RVLT_Delayed_Lang_0 = French (%)" " 147 ( 10.3) "
## "MAT_Lang_0 = French (%)" " 147 ( 10.3) "
## "Animal_Fluency_Lang_0 = French (%)" " 147 ( 10.3) "
## "PASE_TOTAL_0 (mean (SD))" "199.82 (78.37)"
## "PASE_Q1B_0 = 1 (%)" " 510 ( 36.3) "
## "RSTLS_Sleep_0 = 1 (%)" " 422 ( 29.7) "
## "Stratified by Pandemic"
## "" "p" "test" "Missing"
## "n" "" "" " "
## "Age_0 (mean (SD))" "<0.001" "" " 0.0"
## "Sex_0 = M (%)" " NA" "" " 0.0"
## "BMI_0 (mean (SD))" " 0.437" "" " 0.2"
## "Ethnicity_0 = White (%)" " 0.091" "" " 0.0"
## "Relationship_status_0 (%)" " 0.132" "" " 0.1"
## " Divorced" "" "" " "
## " Married" "" "" " "
## " Separated" "" "" " "
## " Single" "" "" " "
## " Widowed" "" "" " "
## "Education4_0 (%)" " 0.096" "" " 0.0"
## " College Degree or Higher" "" "" " "
## " High School Diploma" "" "" " "
## " Less than High School Diploma" "" "" " "
## " Some College" "" "" " "
## "Income_Level_0 (%)" " 0.010" "" " 1.9"
## " <$20k" "" "" " "
## " >$150k" "" "" " "
## " $100-150k" "" "" " "
## " $20-50k" "" "" " "
## " $50-100k" "" "" " "
## "Living_status_0 (%)" " 0.428" "" " 0.0"
## " Apartment/Condo/Townhome" "" "" " "
## " Assisted Living" "" "" " "
## " House" "" "" " "
## " Other" "" "" " "
## "Alcohol_0 (%)" " 0.343" "" " 1.7"
## " Non-drinker" "" "" " "
## " Occasional drinker" "" "" " "
## " Regular drinker (at least once a month)" "" "" " "
## "Smoking_Status_0 (%)" " 0.317" "" " 0.4"
## " Daily Smoker" "" "" " "
## " Former Smoker" "" "" " "
## " Never Smoked" "" "" " "
## " Occasional Smoker" "" "" " "
## "CESD_10_0 (mean (SD))" " 0.032" "" " 0.2"
## "Anxiety_0 = Yes (%)" " 0.905" "" " 0.0"
## "Mood_Disord_0 = Yes (%)" " 0.026" "" " 0.1"
## "Chronic_conditions_0 (mean (SD))" " 0.001" "" " 2.7"
## "RVLT_Immediate_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "RVLT_Delayed_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "Animal_Fluency_Normed_0 (mean (SD))" " 0.202" "" " 0.0"
## "MAT_Normed_0 (mean (SD))" " 0.022" "" " 0.0"
## "RVLT_Immediate_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "RVLT_Delayed_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "MAT_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "Animal_Fluency_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "PASE_TOTAL_0 (mean (SD))" "<0.001" "" "17.5"
## "PASE_Q1B_0 = 1 (%)" " 0.008" "" " 2.3"
## "RSTLS_Sleep_0 = 1 (%)" " 0.348" "" " 0.1"
Females 45-64
Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.FemalesYoung)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
## "Stratified by Pandemic"
## "" "FU2 data collected after COVID-19"
## "n" " 1498"
## "Age_0 (mean (SD))" " 54.09 (5.34)"
## "Sex_0 = F (%)" " 1498 (100.0) "
## "BMI_0 (mean (SD))" " 27.18 (5.75)"
## "Ethnicity_0 = White (%)" " 1460 ( 97.5) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 137 ( 9.1) "
## " Married" " 1107 ( 73.9) "
## " Separated" " 56 ( 3.7) "
## " Single" " 141 ( 9.4) "
## " Widowed" " 57 ( 3.8) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 1064 ( 71.0) "
## " High School Diploma" " 248 ( 16.6) "
## " Less than High School Diploma" " 65 ( 4.3) "
## " Some College" " 121 ( 8.1) "
## "Income_Level_0 (%)" " "
## " <$20k" " 320 ( 22.2) "
## " >$150k" " 26 ( 1.8) "
## " $100-150k" " 71 ( 4.9) "
## " $20-50k" " 550 ( 38.2) "
## " $50-100k" " 473 ( 32.8) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 119 ( 7.9) "
## " Assisted Living" " 1 ( 0.1) "
## " House" " 1371 ( 91.5) "
## " Other" " 7 ( 0.5) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 140 ( 9.6) "
## " Occasional drinker" " 283 ( 19.4) "
## " Regular drinker (at least once a month)" " 1039 ( 71.1) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 138 ( 9.3) "
## " Former Smoker" " 838 ( 56.2) "
## " Never Smoked" " 479 ( 32.1) "
## " Occasional Smoker" " 36 ( 2.4) "
## "CESD_10_0 (mean (SD))" " 5.68 (4.91)"
## "Anxiety_0 = Yes (%)" " 159 ( 10.6) "
## "Mood_Disord_0 = Yes (%)" " 298 ( 19.9) "
## "Chronic_conditions_0 (mean (SD))" " 2.36 (2.05)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.13 (3.67)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.31 (3.50)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 10.23 (3.15)"
## "MAT_Normed_0 (mean (SD))" " 9.92 (3.32)"
## "RVLT_Immediate_Lang_0 = French (%)" " 396 ( 26.4) "
## "RVLT_Delayed_Lang_0 = French (%)" " 396 ( 26.4) "
## "MAT_Lang_0 = French (%)" " 396 ( 26.4) "
## "Animal_Fluency_Lang_0 = French (%)" " 396 ( 26.4) "
## "PASE_TOTAL_0 (mean (SD))" "182.11 (73.93)"
## "PASE_Q1B_0 = 1 (%)" " 507 ( 35.1) "
## "RSTLS_Sleep_0 = 1 (%)" " 582 ( 39.0) "
## "Stratified by Pandemic"
## "" "FU2 data collected before COVID-19"
## "n" " 2246"
## "Age_0 (mean (SD))" " 55.97 (5.32)"
## "Sex_0 = F (%)" " 2246 (100.0) "
## "BMI_0 (mean (SD))" " 27.52 (6.16)"
## "Ethnicity_0 = White (%)" " 2188 ( 97.4) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 250 ( 11.1) "
## " Married" " 1620 ( 72.1) "
## " Separated" " 62 ( 2.8) "
## " Single" " 213 ( 9.5) "
## " Widowed" " 101 ( 4.5) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 1747 ( 77.8) "
## " High School Diploma" " 244 ( 10.9) "
## " Less than High School Diploma" " 82 ( 3.7) "
## " Some College" " 173 ( 7.7) "
## "Income_Level_0 (%)" " "
## " <$20k" " 483 ( 22.3) "
## " >$150k" " 41 ( 1.9) "
## " $100-150k" " 113 ( 5.2) "
## " $20-50k" " 847 ( 39.1) "
## " $50-100k" " 680 ( 31.4) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 217 ( 9.7) "
## " Assisted Living" " 4 ( 0.2) "
## " House" " 2006 ( 89.3) "
## " Other" " 19 ( 0.8) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 224 ( 10.2) "
## " Occasional drinker" " 422 ( 19.3) "
## " Regular drinker (at least once a month)" " 1546 ( 70.5) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 187 ( 8.3) "
## " Former Smoker" " 1200 ( 53.5) "
## " Never Smoked" " 810 ( 36.1) "
## " Occasional Smoker" " 45 ( 2.0) "
## "CESD_10_0 (mean (SD))" " 5.26 (4.56)"
## "Anxiety_0 = Yes (%)" " 180 ( 8.0) "
## "Mood_Disord_0 = Yes (%)" " 423 ( 18.8) "
## "Chronic_conditions_0 (mean (SD))" " 2.59 (2.14)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.65 (3.81)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.85 (3.61)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 10.42 (3.13)"
## "MAT_Normed_0 (mean (SD))" " 10.16 (3.38)"
## "RVLT_Immediate_Lang_0 = French (%)" " 304 ( 13.5) "
## "RVLT_Delayed_Lang_0 = French (%)" " 304 ( 13.5) "
## "MAT_Lang_0 = French (%)" " 304 ( 13.5) "
## "Animal_Fluency_Lang_0 = French (%)" " 304 ( 13.5) "
## "PASE_TOTAL_0 (mean (SD))" "170.96 (72.48)"
## "PASE_Q1B_0 = 1 (%)" " 830 ( 37.3) "
## "RSTLS_Sleep_0 = 1 (%)" " 829 ( 37.0) "
## "Stratified by Pandemic"
## "" "p" "test" "Missing"
## "n" "" "" " "
## "Age_0 (mean (SD))" "<0.001" "" " 0.0"
## "Sex_0 = F (%)" " NA" "" " 0.0"
## "BMI_0 (mean (SD))" " 0.084" "" " 0.9"
## "Ethnicity_0 = White (%)" " 1.000" "" " 0.0"
## "Relationship_status_0 (%)" " 0.109" "" " 0.0"
## " Divorced" "" "" " "
## " Married" "" "" " "
## " Separated" "" "" " "
## " Single" "" "" " "
## " Widowed" "" "" " "
## "Education4_0 (%)" "<0.001" "" " 0.0"
## " College Degree or Higher" "" "" " "
## " High School Diploma" "" "" " "
## " Less than High School Diploma" "" "" " "
## " Some College" "" "" " "
## "Income_Level_0 (%)" " 0.920" "" " 3.7"
## " <$20k" "" "" " "
## " >$150k" "" "" " "
## " $100-150k" "" "" " "
## " $20-50k" "" "" " "
## " $50-100k" "" "" " "
## "Living_status_0 (%)" " 0.105" "" " 0.0"
## " Apartment/Condo/Townhome" "" "" " "
## " Assisted Living" "" "" " "
## " House" "" "" " "
## " Other" "" "" " "
## "Alcohol_0 (%)" " 0.817" "" " 2.4"
## " Non-drinker" "" "" " "
## " Occasional drinker" "" "" " "
## " Regular drinker (at least once a month)" "" "" " "
## "Smoking_Status_0 (%)" " 0.076" "" " 0.3"
## " Daily Smoker" "" "" " "
## " Former Smoker" "" "" " "
## " Never Smoked" "" "" " "
## " Occasional Smoker" "" "" " "
## "CESD_10_0 (mean (SD))" " 0.008" "" " 0.2"
## "Anxiety_0 = Yes (%)" " 0.007" "" " 0.1"
## "Mood_Disord_0 = Yes (%)" " 0.443" "" " 0.1"
## "Chronic_conditions_0 (mean (SD))" " 0.001" "" " 3.3"
## "RVLT_Immediate_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "RVLT_Delayed_Normed_0 (mean (SD))" "<0.001" "" " 0.0"
## "Animal_Fluency_Normed_0 (mean (SD))" " 0.062" "" " 0.0"
## "MAT_Normed_0 (mean (SD))" " 0.029" "" " 0.0"
## "RVLT_Immediate_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "RVLT_Delayed_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "MAT_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "Animal_Fluency_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "PASE_TOTAL_0 (mean (SD))" "<0.001" "" "18.8"
## "PASE_Q1B_0 = 1 (%)" " 0.189" "" " 2.0"
## "RSTLS_Sleep_0 = 1 (%)" " 0.231" "" " 0.2"
Males 65+
Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.MalesOld)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
## "Stratified by Pandemic"
## "" "FU2 data collected after COVID-19"
## "n" " 824"
## "Age_0 (mean (SD))" " 73.50 (5.55)"
## "Sex_0 = M (%)" " 824 (100.0) "
## "BMI_0 (mean (SD))" " 27.23 (4.10)"
## "Ethnicity_0 = White (%)" " 802 ( 97.3) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 47 ( 5.7) "
## " Married" " 663 ( 80.6) "
## " Separated" " 18 ( 2.2) "
## " Single" " 33 ( 4.0) "
## " Widowed" " 62 ( 7.5) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 542 ( 65.8) "
## " High School Diploma" " 113 ( 13.7) "
## " Less than High School Diploma" " 103 ( 12.5) "
## " Some College" " 66 ( 8.0) "
## "Income_Level_0 (%)" " "
## " <$20k" " 79 ( 9.8) "
## " >$150k" " 19 ( 2.4) "
## " $100-150k" " 48 ( 6.0) "
## " $20-50k" " 408 ( 50.7) "
## " $50-100k" " 250 ( 31.1) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 125 ( 15.2) "
## " Assisted Living" " 4 ( 0.5) "
## " House" " 690 ( 83.7) "
## " Other" " 5 ( 0.6) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 89 ( 11.1) "
## " Occasional drinker" " 92 ( 11.5) "
## " Regular drinker (at least once a month)" " 618 ( 77.3) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 22 ( 2.7) "
## " Former Smoker" " 620 ( 75.7) "
## " Never Smoked" " 171 ( 20.9) "
## " Occasional Smoker" " 6 ( 0.7) "
## "CESD_10_0 (mean (SD))" " 4.39 (3.83)"
## "Anxiety_0 = Yes (%)" " 22 ( 2.7) "
## "Mood_Disord_0 = Yes (%)" " 46 ( 5.6) "
## "Chronic_conditions_0 (mean (SD))" " 3.24 (2.21)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.32 (4.11)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.83 (3.91)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 10.16 (2.88)"
## "MAT_Normed_0 (mean (SD))" " 9.69 (3.57)"
## "RVLT_Immediate_Lang_0 = French (%)" " 220 ( 26.7) "
## "RVLT_Delayed_Lang_0 = French (%)" " 220 ( 26.7) "
## "MAT_Lang_0 = French (%)" " 220 ( 26.7) "
## "Animal_Fluency_Lang_0 = French (%)" " 220 ( 26.7) "
## "PASE_TOTAL_0 (mean (SD))" "138.16 (60.96)"
## "PASE_Q1B_0 = 1 (%)" " 355 ( 44.3) "
## "RSTLS_Sleep_0 = 1 (%)" " 232 ( 28.3) "
## "Stratified by Pandemic"
## "" "FU2 data collected before COVID-19"
## "n" " 1187"
## "Age_0 (mean (SD))" " 72.09 (5.35)"
## "Sex_0 = M (%)" " 1187 (100.0) "
## "BMI_0 (mean (SD))" " 27.19 (3.85)"
## "Ethnicity_0 = White (%)" " 1156 ( 97.4) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 73 ( 6.1) "
## " Married" " 937 ( 78.9) "
## " Separated" " 16 ( 1.3) "
## " Single" " 50 ( 4.2) "
## " Widowed" " 111 ( 9.4) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 870 ( 73.3) "
## " High School Diploma" " 125 ( 10.5) "
## " Less than High School Diploma" " 100 ( 8.4) "
## " Some College" " 92 ( 7.8) "
## "Income_Level_0 (%)" " "
## " <$20k" " 89 ( 7.7) "
## " >$150k" " 47 ( 4.1) "
## " $100-150k" " 71 ( 6.1) "
## " $20-50k" " 547 ( 47.3) "
## " $50-100k" " 403 ( 34.8) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 152 ( 12.8) "
## " Assisted Living" " 9 ( 0.8) "
## " House" " 1009 ( 85.0) "
## " Other" " 17 ( 1.4) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 124 ( 10.8) "
## " Occasional drinker" " 118 ( 10.2) "
## " Regular drinker (at least once a month)" " 911 ( 79.0) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 39 ( 3.3) "
## " Former Smoker" " 865 ( 73.4) "
## " Never Smoked" " 267 ( 22.7) "
## " Occasional Smoker" " 7 ( 0.6) "
## "CESD_10_0 (mean (SD))" " 4.17 (3.69)"
## "Anxiety_0 = Yes (%)" " 45 ( 3.8) "
## "Mood_Disord_0 = Yes (%)" " 87 ( 7.3) "
## "Chronic_conditions_0 (mean (SD))" " 3.32 (2.30)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.62 (3.68)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 10.91 (3.67)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 10.26 (2.87)"
## "MAT_Normed_0 (mean (SD))" " 10.04 (3.53)"
## "RVLT_Immediate_Lang_0 = French (%)" " 88 ( 7.4) "
## "RVLT_Delayed_Lang_0 = French (%)" " 88 ( 7.4) "
## "MAT_Lang_0 = French (%)" " 88 ( 7.4) "
## "Animal_Fluency_Lang_0 = French (%)" " 88 ( 7.4) "
## "PASE_TOTAL_0 (mean (SD))" "136.93 (63.79)"
## "PASE_Q1B_0 = 1 (%)" " 541 ( 45.8) "
## "RSTLS_Sleep_0 = 1 (%)" " 345 ( 29.1) "
## "Stratified by Pandemic"
## "" "p" "test" "Missing"
## "n" "" "" " "
## "Age_0 (mean (SD))" "<0.001" "" " 0.0"
## "Sex_0 = M (%)" " NA" "" " 0.0"
## "BMI_0 (mean (SD))" " 0.834" "" " 0.2"
## "Ethnicity_0 = White (%)" " 1.000" "" " 0.0"
## "Relationship_status_0 (%)" " 0.373" "" " 0.0"
## " Divorced" "" "" " "
## " Married" "" "" " "
## " Separated" "" "" " "
## " Single" "" "" " "
## " Widowed" "" "" " "
## "Education4_0 (%)" " 0.001" "" " 0.0"
## " College Degree or Higher" "" "" " "
## " High School Diploma" "" "" " "
## " Less than High School Diploma" "" "" " "
## " Some College" "" "" " "
## "Income_Level_0 (%)" " 0.044" "" " 2.5"
## " <$20k" "" "" " "
## " >$150k" "" "" " "
## " $100-150k" "" "" " "
## " $20-50k" "" "" " "
## " $50-100k" "" "" " "
## "Living_status_0 (%)" " 0.130" "" " 0.0"
## " Apartment/Condo/Townhome" "" "" " "
## " Assisted Living" "" "" " "
## " House" "" "" " "
## " Other" "" "" " "
## "Alcohol_0 (%)" " 0.622" "" " 2.9"
## " Non-drinker" "" "" " "
## " Occasional drinker" "" "" " "
## " Regular drinker (at least once a month)" "" "" " "
## "Smoking_Status_0 (%)" " 0.616" "" " 0.7"
## " Daily Smoker" "" "" " "
## " Former Smoker" "" "" " "
## " Never Smoked" "" "" " "
## " Occasional Smoker" "" "" " "
## "CESD_10_0 (mean (SD))" " 0.199" "" " 0.5"
## "Anxiety_0 = Yes (%)" " 0.208" "" " 0.1"
## "Mood_Disord_0 = Yes (%)" " 0.145" "" " 0.1"
## "Chronic_conditions_0 (mean (SD))" " 0.475" "" " 4.6"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 0.083" "" " 0.0"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 0.609" "" " 0.0"
## "Animal_Fluency_Normed_0 (mean (SD))" " 0.446" "" " 0.0"
## "MAT_Normed_0 (mean (SD))" " 0.032" "" " 0.0"
## "RVLT_Immediate_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "RVLT_Delayed_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "MAT_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "Animal_Fluency_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "PASE_TOTAL_0 (mean (SD))" " 0.694" "" "18.3"
## "PASE_Q1B_0 = 1 (%)" " 0.516" "" " 1.4"
## "RSTLS_Sleep_0 = 1 (%)" " 0.718" "" " 0.3"
Females 65+
Table1_Final_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=BL.FemalesOld)
print(Table1_Final_stratify,contDigits=2,missing=TRUE,quote=TRUE)
## "Stratified by Pandemic"
## "" "FU2 data collected after COVID-19"
## "n" " 827"
## "Age_0 (mean (SD))" " 73.52 (5.56)"
## "Sex_0 = F (%)" " 827 (100.0) "
## "BMI_0 (mean (SD))" " 27.31 (5.19)"
## "Ethnicity_0 = White (%)" " 816 ( 98.7) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 98 ( 11.9) "
## " Married" " 415 ( 50.2) "
## " Separated" " 12 ( 1.5) "
## " Single" " 46 ( 5.6) "
## " Widowed" " 256 ( 31.0) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 444 ( 53.7) "
## " High School Diploma" " 167 ( 20.2) "
## " Less than High School Diploma" " 148 ( 17.9) "
## " Some College" " 68 ( 8.2) "
## "Income_Level_0 (%)" " "
## " <$20k" " 261 ( 33.5) "
## " >$150k" " 6 ( 0.8) "
## " $100-150k" " 10 ( 1.3) "
## " $20-50k" " 399 ( 51.2) "
## " $50-100k" " 104 ( 13.3) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 189 ( 22.9) "
## " Assisted Living" " 17 ( 2.1) "
## " House" " 608 ( 73.5) "
## " Other" " 13 ( 1.6) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 109 ( 14.1) "
## " Occasional drinker" " 186 ( 24.1) "
## " Regular drinker (at least once a month)" " 477 ( 61.8) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 33 ( 4.0) "
## " Former Smoker" " 459 ( 55.7) "
## " Never Smoked" " 324 ( 39.3) "
## " Occasional Smoker" " 8 ( 1.0) "
## "CESD_10_0 (mean (SD))" " 5.33 (4.22)"
## "Anxiety_0 = Yes (%)" " 54 ( 6.5) "
## "Mood_Disord_0 = Yes (%)" " 97 ( 11.7) "
## "Chronic_conditions_0 (mean (SD))" " 4.16 (2.60)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 10.92 (3.97)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 11.36 (3.91)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 10.17 (3.03)"
## "MAT_Normed_0 (mean (SD))" " 9.94 (3.71)"
## "RVLT_Immediate_Lang_0 = French (%)" " 218 ( 26.4) "
## "RVLT_Delayed_Lang_0 = French (%)" " 218 ( 26.4) "
## "MAT_Lang_0 = French (%)" " 218 ( 26.4) "
## "Animal_Fluency_Lang_0 = French (%)" " 218 ( 26.4) "
## "PASE_TOTAL_0 (mean (SD))" "119.62 (54.01)"
## "PASE_Q1B_0 = 1 (%)" " 383 ( 47.0) "
## "RSTLS_Sleep_0 = 1 (%)" " 262 ( 31.7) "
## "Stratified by Pandemic"
## "" "FU2 data collected before COVID-19"
## "n" " 1317"
## "Age_0 (mean (SD))" " 72.42 (5.62)"
## "Sex_0 = F (%)" " 1317 (100.0) "
## "BMI_0 (mean (SD))" " 27.00 (5.16)"
## "Ethnicity_0 = White (%)" " 1293 ( 98.2) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 193 ( 14.7) "
## " Married" " 660 ( 50.2) "
## " Separated" " 19 ( 1.4) "
## " Single" " 79 ( 6.0) "
## " Widowed" " 364 ( 27.7) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 975 ( 74.0) "
## " High School Diploma" " 157 ( 11.9) "
## " Less than High School Diploma" " 106 ( 8.0) "
## " Some College" " 79 ( 6.0) "
## "Income_Level_0 (%)" " "
## " <$20k" " 315 ( 25.7) "
## " >$150k" " 9 ( 0.7) "
## " $100-150k" " 19 ( 1.5) "
## " $20-50k" " 667 ( 54.4) "
## " $50-100k" " 216 ( 17.6) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 272 ( 20.7) "
## " Assisted Living" " 23 ( 1.7) "
## " House" " 1006 ( 76.4) "
## " Other" " 16 ( 1.2) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 163 ( 13.2) "
## " Occasional drinker" " 269 ( 21.8) "
## " Regular drinker (at least once a month)" " 801 ( 65.0) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 52 ( 4.0) "
## " Former Smoker" " 763 ( 58.3) "
## " Never Smoked" " 484 ( 37.0) "
## " Occasional Smoker" " 10 ( 0.8) "
## "CESD_10_0 (mean (SD))" " 5.13 (4.43)"
## "Anxiety_0 = Yes (%)" " 90 ( 6.8) "
## "Mood_Disord_0 = Yes (%)" " 156 ( 11.9) "
## "Chronic_conditions_0 (mean (SD))" " 3.99 (2.59)"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 11.18 (3.99)"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 11.45 (3.84)"
## "Animal_Fluency_Normed_0 (mean (SD))" " 10.46 (2.87)"
## "MAT_Normed_0 (mean (SD))" " 9.97 (3.60)"
## "RVLT_Immediate_Lang_0 = French (%)" " 104 ( 7.9) "
## "RVLT_Delayed_Lang_0 = French (%)" " 104 ( 7.9) "
## "MAT_Lang_0 = French (%)" " 104 ( 7.9) "
## "Animal_Fluency_Lang_0 = French (%)" " 104 ( 7.9) "
## "PASE_TOTAL_0 (mean (SD))" "115.93 (56.88)"
## "PASE_Q1B_0 = 1 (%)" " 549 ( 41.9) "
## "RSTLS_Sleep_0 = 1 (%)" " 435 ( 33.1) "
## "Stratified by Pandemic"
## "" "p" "test" "Missing"
## "n" "" "" " "
## "Age_0 (mean (SD))" "<0.001" "" " 0.0"
## "Sex_0 = F (%)" " NA" "" " 0.0"
## "BMI_0 (mean (SD))" " 0.184" "" " 0.7"
## "Ethnicity_0 = White (%)" " 0.484" "" " 0.0"
## "Relationship_status_0 (%)" " 0.283" "" " 0.1"
## " Divorced" "" "" " "
## " Married" "" "" " "
## " Separated" "" "" " "
## " Single" "" "" " "
## " Widowed" "" "" " "
## "Education4_0 (%)" "<0.001" "" " 0.0"
## " College Degree or Higher" "" "" " "
## " High School Diploma" "" "" " "
## " Less than High School Diploma" "" "" " "
## " Some College" "" "" " "
## "Income_Level_0 (%)" " 0.002" "" " 6.4"
## " <$20k" "" "" " "
## " >$150k" "" "" " "
## " $100-150k" "" "" " "
## " $20-50k" "" "" " "
## " $50-100k" "" "" " "
## "Living_status_0 (%)" " 0.487" "" " 0.0"
## " Apartment/Condo/Townhome" "" "" " "
## " Assisted Living" "" "" " "
## " House" "" "" " "
## " Other" "" "" " "
## "Alcohol_0 (%)" " 0.347" "" " 6.5"
## " Non-drinker" "" "" " "
## " Occasional drinker" "" "" " "
## " Regular drinker (at least once a month)" "" "" " "
## "Smoking_Status_0 (%)" " 0.663" "" " 0.5"
## " Daily Smoker" "" "" " "
## " Former Smoker" "" "" " "
## " Never Smoked" "" "" " "
## " Occasional Smoker" "" "" " "
## "CESD_10_0 (mean (SD))" " 0.294" "" " 0.4"
## "Anxiety_0 = Yes (%)" " 0.855" "" " 0.1"
## "Mood_Disord_0 = Yes (%)" " 0.985" "" " 0.0"
## "Chronic_conditions_0 (mean (SD))" " 0.166" "" " 5.5"
## "RVLT_Immediate_Normed_0 (mean (SD))" " 0.140" "" " 0.0"
## "RVLT_Delayed_Normed_0 (mean (SD))" " 0.589" "" " 0.0"
## "Animal_Fluency_Normed_0 (mean (SD))" " 0.026" "" " 0.0"
## "MAT_Normed_0 (mean (SD))" " 0.837" "" " 0.0"
## "RVLT_Immediate_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "RVLT_Delayed_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "MAT_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "Animal_Fluency_Lang_0 = French (%)" "<0.001" "" " 0.0"
## "PASE_TOTAL_0 (mean (SD))" " 0.193" "" "23.2"
## "PASE_Q1B_0 = 1 (%)" " 0.025" "" " 0.9"
## "RSTLS_Sleep_0 = 1 (%)" " 0.544" "" " 0.1"
Linear mixed model set-up
Tracking.data.short<-Tracking.Adjusted_Final[c(1,4:12,14,15,18:21,146,147,98,115,132,102,119,136,106,123,140,110,127,144,13,44,75,22,53,84,23,54,85)]
Tracking.data.short2<-rename(Tracking.data.short, c("Age"="Age_0","Sex"="Sex_0","Ethnicity"="Ethnicity_0","Relationshipstatus"="Relationship_status_0",
"Education"="Education4_0", "IncomeLevel"="Income_Level_0", "Livingstatus"="Living_status_0",
"Alcohol"="Alcohol_0", "SmokingStatus"="Smoking_Status_0","Anxiety"="Anxiety_0","MoodDisord"="Mood_Disord_0",
"Chronicconditions"="Chronic_conditions_0", "BMI"="BMI_0","PASE_Sit_0"="PASE_Q1B_0","PASE_Sit_1"="PASE_Q1B_1","PASE_Sit_2"="PASE_Q1B_2"))
Tracking.data.short2$PASE_TOTALbaseline <- Tracking.data.short2$PASE_TOTAL_0
Tracking.data.short3<-Tracking.data.short2[c(1:18,40,19:39)]
Tracking.data.short4<-Tracking.data.short3[c(1:18,20,23,26,29,32,35,38,21:22,24:25,27:28,30:31,33:34,36:37,39:40)]
colnames(Tracking.data.short3) <- (gsub("_2",".3",colnames(Tracking.data.short3)))
colnames(Tracking.data.short3) <- (gsub("_1",".2",colnames(Tracking.data.short3)))
colnames(Tracking.data.short3) <- (gsub("_0",".1",colnames(Tracking.data.short3)))
colnames(Tracking.data.short4) <- (gsub("_2",".2",colnames(Tracking.data.short4)))
colnames(Tracking.data.short4) <- (gsub("_1",".1",colnames(Tracking.data.short4)))
colnames(Tracking.data.short4) <- (gsub("_0","baseline",colnames(Tracking.data.short4)))
Tracking.data_long <- reshape(as.data.frame(Tracking.data.short3),idvar="ID",varying=20:40,direction="long",sep=".") #reshape data into long format (3 timepoints)
Tracking.data_long_2 <- reshape(as.data.frame(Tracking.data.short4),idvar="ID",varying=26:39,direction="long",sep=".") #reshape data into long format (3 timepoints)
Indexed time as a categorical factor
#Treat time as a fixed effect
Tracking.data_long$timefactor<-as.factor(Tracking.data_long$time)
Tracking.data_long_2$timefactor<-as.factor(Tracking.data_long_2$time)
Increase the data matrix to support the models
emm_options(rg.limit = 150000)
emm_options(opt.digits = FALSE)
Contrast statements
#Contrast 1: Group differences from baseline to FU1
c1=matrix(c(0,1,0,-1,0,0))
c2=matrix(c(1,0,-1,0,0,0))
c1st=c1 - c2
#Contrast 2: Group differences from FU1 to FU2
c1=matrix(c(0,0,0,1,0,-1))
c2=matrix(c(0,0,1,0,-1,0))
c2nd = c1 - c2
#Contrast 3: Group differences from baseline to FU2
c1=matrix(c(0,1,0,0,0,-1))
c2=matrix(c(1,0,0,0,-1,0))
c3rd = c1 - c2
For each of the cognitive models, we used the normalized scores as the dependent variable. Baseline is not adjusted and no covariates were included.
track.modelRVLT_imm_3<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + (1|ID), data= Tracking.data_long)
summary(track.modelRVLT_imm_3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Immediate_Normed ~ timefactor * Pandemic + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 178154.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6062 -0.5765 -0.0440 0.5241 5.0473
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.477 2.340
## Residual 8.532 2.921
## Number of obs: 33347, groups: ID, 11355
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 1.013e+01 5.200e-02
## timefactor2 6.845e-01 5.784e-02
## timefactor3 9.738e-01 5.845e-02
## PandemicFU2 data collected before COVID-19 5.726e-01 7.052e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -2.406e-01 7.846e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -3.149e-01 7.891e-02
## df t value
## (Intercept) 2.566e+04 194.776
## timefactor2 2.209e+04 11.834
## timefactor3 2.222e+04 16.662
## PandemicFU2 data collected before COVID-19 2.566e+04 8.119
## timefactor2:PandemicFU2 data collected before COVID-19 2.209e+04 -3.067
## timefactor3:PandemicFU2 data collected before COVID-19 2.217e+04 -3.991
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## timefactor3 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 4.90e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.00217 **
## timefactor3:PandemicFU2 data collected before COVID-19 6.59e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tmfct2 tmfct3 PFdcbC t2dcbC
## timefactor2 -0.548
## timefactor3 -0.542 0.487
## PFU2dcbCOVI -0.737 0.404 0.400
## t2:PFU2dcbC 0.404 -0.737 -0.359 -0.547
## t3:PFU2dcbC 0.401 -0.361 -0.741 -0.544 0.490
lsmeans(track.modelRVLT_imm_3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.12814 0.05199884 Inf 10.02623 10.23006
## FU2 data collected before COVID-19 10.70071 0.04763401 Inf 10.60735 10.79407
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.81261 0.05249420 Inf 10.70973 10.91550
## FU2 data collected before COVID-19 11.14457 0.04811768 Inf 11.05026 11.23887
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.10199 0.05316238 Inf 10.99779 11.20619
## FU2 data collected before COVID-19 11.35962 0.04811768 Inf 11.26531 11.45393
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(track.modelRVLT_imm_3, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.5725642 0.07051863 Inf -8.119 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3319512 0.07121062 Inf -4.662 <.0001
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2576324 0.07170460 Inf -3.593 0.0003
##
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(track.modelRVLT_imm_3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.5725642 0.07051863 Inf -0.7107782 -0.4343503
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3319512 0.07121062 Inf -0.4715214 -0.1923809
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2576324 0.07170460 Inf -0.3981709 -0.1170940
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTimmediate_lsmeans_3 <- summary(lsmeans(track.modelRVLT_imm_3, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTimmediate_lsmeans_3$Time<-NA
RVLTimmediate_lsmeans_3$Time[RVLTimmediate_lsmeans_3$timefactor==1]<-"Baseline"
RVLTimmediate_lsmeans_3$Time[RVLTimmediate_lsmeans_3$timefactor==2]<-"Follow-up 1"
RVLTimmediate_lsmeans_3$Time[RVLTimmediate_lsmeans_3$timefactor==3]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_3, aes(x = Time, y = lsmean, group = Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from Baseline to FU2 by Pandemic status") +
theme_bw()
lsmeans.RVLTImm1 <- lsmeans(track.modelRVLT_imm_3, ~ Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33347)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.RVLTImm1,list(c1st,c2nd,c3rd),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.24061305 0.07845730 Inf
## structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) 0.07431874 0.07949815 Inf
## structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) 0.31493179 0.07890593 Inf
## z.ratio p.value
## 3.067 0.0022
## 0.935 0.3499
## 3.991 0.0001
##
## Degrees-of-freedom method: asymptotic
modelRVLT_del_3<- lmer(RVLT_Delayed_Normed ~ timefactor*Pandemic + (1|ID), data= Tracking.data_long)
summary(modelRVLT_del_3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 175144.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8201 -0.5593 -0.0388 0.5068 5.0673
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.794 2.407
## Residual 7.766 2.787
## Number of obs: 33150, groups: ID, 11355
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 1.045e+01 5.116e-02
## timefactor2 5.168e-01 5.538e-02
## timefactor3 1.087e+00 5.598e-02
## PandemicFU2 data collected before COVID-19 4.873e-01 6.938e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -5.271e-02 7.510e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -3.268e-01 7.553e-02
## df t value
## (Intercept) 2.447e+04 204.162
## timefactor2 2.195e+04 9.331
## timefactor3 2.207e+04 19.423
## PandemicFU2 data collected before COVID-19 2.447e+04 7.023
## timefactor2:PandemicFU2 data collected before COVID-19 2.195e+04 -0.702
## timefactor3:PandemicFU2 data collected before COVID-19 2.201e+04 -4.327
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## timefactor3 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 2.23e-12 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.483
## timefactor3:PandemicFU2 data collected before COVID-19 1.52e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tmfct2 tmfct3 PFdcbC t2dcbC
## timefactor2 -0.529
## timefactor3 -0.523 0.484
## PFU2dcbCOVI -0.737 0.390 0.386
## t2:PFU2dcbC 0.390 -0.737 -0.357 -0.529
## t3:PFU2dcbC 0.388 -0.359 -0.741 -0.526 0.487
lsmeans(modelRVLT_del_3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.44504 0.05116045 Inf 10.34477 10.54531
## FU2 data collected before COVID-19 10.93232 0.04686600 Inf 10.84046 11.02417
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.96183 0.05183315 Inf 10.86024 11.06342
## FU2 data collected before COVID-19 11.39640 0.04746859 Inf 11.30337 11.48944
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.53230 0.05246753 Inf 11.42947 11.63514
## FU2 data collected before COVID-19 11.69276 0.04745071 Inf 11.59976 11.78576
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_3, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4872765 0.06938165 Inf -7.023 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4345710 0.07028473 Inf -6.183 <.0001
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1604577 0.07074186 Inf -2.268 0.0233
##
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_del_3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4872765 0.06938165 Inf -0.6232620 -0.3512909
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4345710 0.07028473 Inf -0.5723265 -0.2968154
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1604577 0.07074186 Inf -0.2991092 -0.0218062
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTdelayed_lsmeans_3 <- summary(lsmeans(modelRVLT_del_3, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTdelayed_lsmeans_3$Time<-NA
RVLTdelayed_lsmeans_3$Time[RVLTdelayed_lsmeans_3$timefactor==1]<-"Baseline"
RVLTdelayed_lsmeans_3$Time[RVLTdelayed_lsmeans_3$timefactor==2]<-"Follow-up 1"
RVLTdelayed_lsmeans_3$Time[RVLTdelayed_lsmeans_3$timefactor==3]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_3, aes(x = Time, y = lsmean, group = Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from Baseline to FU2 by Pandemic status") +
theme_bw()
lsmeans.RVLTDel1 <- lsmeans(modelRVLT_del_3, ~ Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33150' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33150)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.RVLTDel1,list(c1st,c2nd,c3rd),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.0527055 0.07509956 Inf
## structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) 0.2741133 0.07631383 Inf
## structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) 0.3268188 0.07552756 Inf
## z.ratio p.value
## 0.702 0.4828
## 3.592 0.0003
## 4.327 <.0001
##
## Degrees-of-freedom method: asymptotic
modelMAT_3<- lmer(MAT_Normed~ timefactor*Pandemic + (1|ID), data= Tracking.data_long)
summary(modelMAT_3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 167568.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0453 -0.4679 -0.0205 0.4174 4.9277
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.319 2.306
## Residual 7.729 2.780
## Number of obs: 31829, groups: ID, 11355
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 9.820e+00 5.018e-02
## timefactor2 1.179e+00 5.656e-02
## timefactor3 -2.344e-01 5.702e-02
## PandemicFU2 data collected before COVID-19 2.448e-01 6.806e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -5.428e-01 7.657e-02
## timefactor3:PandemicFU2 data collected before COVID-19 1.161e-02 7.691e-02
## df t value
## (Intercept) 2.428e+04 195.679
## timefactor2 2.092e+04 20.843
## timefactor3 2.101e+04 -4.111
## PandemicFU2 data collected before COVID-19 2.428e+04 3.597
## timefactor2:PandemicFU2 data collected before COVID-19 2.089e+04 -7.089
## timefactor3:PandemicFU2 data collected before COVID-19 2.094e+04 0.151
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## timefactor3 3.96e-05 ***
## PandemicFU2 data collected before COVID-19 0.000322 ***
## timefactor2:PandemicFU2 data collected before COVID-19 1.39e-12 ***
## timefactor3:PandemicFU2 data collected before COVID-19 0.880023
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tmfct2 tmfct3 PFdcbC t2dcbC
## timefactor2 -0.526
## timefactor3 -0.521 0.465
## PFU2dcbCOVI -0.737 0.388 0.384
## t2:PFU2dcbC 0.388 -0.739 -0.343 -0.527
## t3:PFU2dcbC 0.387 -0.345 -0.741 -0.524 0.468
lsmeans(modelMAT_3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL
## FU2 data collected after COVID-19 9.819871 0.05018368 Inf 9.721513
## FU2 data collected before COVID-19 10.064693 0.04597121 Inf 9.974591
## asymp.UCL
## 9.918229
## 10.154795
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL
## FU2 data collected after COVID-19 10.998711 0.05228331 Inf 10.896238
## FU2 data collected before COVID-19 10.700711 0.04767891 Inf 10.607262
## asymp.UCL
## 11.101185
## 10.794160
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL
## FU2 data collected after COVID-19 9.585467 0.05278333 Inf 9.482013
## FU2 data collected before COVID-19 9.841897 0.04767230 Inf 9.748461
## asymp.UCL
## 9.688920
## 9.935333
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_3, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2448220 0.06805699 Inf -3.597 0.0003
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.2980000 0.07075891 Inf 4.211 <.0001
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2564302 0.07112474 Inf -3.605 0.0003
##
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelMAT_3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2448220 0.06805699 Inf -0.3782112 -0.1114327
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.2980000 0.07075891 Inf 0.1593151 0.4366849
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2564302 0.07112474 Inf -0.3958321 -0.1170283
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
MAT_lsmeans_3 <- summary(lsmeans(modelMAT_3, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.
MAT_lsmeans_3$Time<-NA
MAT_lsmeans_3$Time[MAT_lsmeans_3$timefactor==1]<-"Baseline"
MAT_lsmeans_3$Time[MAT_lsmeans_3$timefactor==2]<-"Follow-up 1"
MAT_lsmeans_3$Time[MAT_lsmeans_3$timefactor==3]<-"Follow-up 2"
ggplot(MAT_lsmeans_3, aes(x = Time, y = lsmean, group = Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from Baseline to FU2 by Pandemic status") +
theme_bw()
lsmeans.MAT1 <- lsmeans(modelMAT_3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 31829' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 31829)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.MAT1,list(c1st,c2nd,c3rd),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.5428220 0.07656705 Inf
## structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -0.5544302 0.07917868 Inf
## structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -0.0116082 0.07690526 Inf
## z.ratio p.value
## 7.089 <.0001
## -7.002 <.0001
## -0.151 0.8800
##
## Degrees-of-freedom method: asymptotic
modelAnimals_3<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + (1|ID), data= Tracking.data_long)
summary(modelAnimals_3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Animal_Fluency_Normed ~ timefactor * Pandemic + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 156896.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3392 -0.5628 -0.0186 0.5482 4.5591
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.041 2.245
## Residual 3.656 1.912
## Number of obs: 33494, groups: ID, 11355
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 1.019e+01 4.097e-02
## timefactor2 -8.038e-02 3.781e-02
## timefactor3 -1.899e-02 3.826e-02
## PandemicFU2 data collected before COVID-19 1.883e-01 5.556e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -2.083e-02 5.126e-02
## timefactor3:PandemicFU2 data collected before COVID-19 1.048e-02 5.160e-02
## df t value
## (Intercept) 2.020e+04 248.636
## timefactor2 2.220e+04 -2.126
## timefactor3 2.229e+04 -0.496
## PandemicFU2 data collected before COVID-19 2.020e+04 3.388
## timefactor2:PandemicFU2 data collected before COVID-19 2.219e+04 -0.406
## timefactor3:PandemicFU2 data collected before COVID-19 2.225e+04 0.203
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 0.033500 *
## timefactor3 0.619625
## PandemicFU2 data collected before COVID-19 0.000705 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.684453
## timefactor3:PandemicFU2 data collected before COVID-19 0.839056
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tmfct2 tmfct3 PFdcbC t2dcbC
## timefactor2 -0.456
## timefactor3 -0.450 0.488
## PFU2dcbCOVI -0.737 0.336 0.332
## t2:PFU2dcbC 0.336 -0.738 -0.360 -0.456
## t3:PFU2dcbC 0.334 -0.362 -0.741 -0.453 0.491
lsmeans(modelAnimals_3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.18698 0.04097150 Inf 10.10667 10.26728
## FU2 data collected before COVID-19 10.37523 0.03753232 Inf 10.30167 10.44879
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.10659 0.04119001 Inf 10.02586 10.18732
## FU2 data collected before COVID-19 10.27402 0.03771754 Inf 10.20009 10.34794
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.16798 0.04161010 Inf 10.08643 10.24953
## FU2 data collected before COVID-19 10.36672 0.03772654 Inf 10.29278 10.44066
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_3, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1882578 0.05556383 Inf -3.388 0.0007
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1674258 0.05585006 Inf -2.998 0.0027
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1987389 0.05616665 Inf -3.538 0.0004
##
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelAnimals_3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1882578 0.05556383 Inf -0.2971609 -0.07935470
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1674258 0.05585006 Inf -0.2768899 -0.05796166
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1987389 0.05616665 Inf -0.3088235 -0.08865428
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
Animals_lsmeans_3 <- summary(lsmeans(modelAnimals_3, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.
Animals_lsmeans_3$Time<-NA
Animals_lsmeans_3$Time[Animals_lsmeans_3$timefactor==1]<-"Baseline"
Animals_lsmeans_3$Time[Animals_lsmeans_3$timefactor==2]<-"Follow-up 1"
Animals_lsmeans_3$Time[Animals_lsmeans_3$timefactor==3]<-"Follow-up 2"
ggplot(Animals_lsmeans_3, aes(x = Time, y = lsmean, group = Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from Baseline to FU2 by Pandemic status") +
theme_bw()
lsmeans.Animals1 <- lsmeans(modelAnimals_3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 33494' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 33494)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.Animals1,list(c1st,c2nd,c3rd),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.02083203 0.05125999 Inf
## structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -0.03131311 0.05189247 Inf
## structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -0.01048108 0.05160474 Inf
## z.ratio p.value
## 0.406 0.6844
## -0.603 0.5462
## -0.203 0.8391
##
## Degrees-of-freedom method: asymptotic
Contrast statements
#Contrast 1: Group differences from baseline to FU1
c1=matrix(c(0,1,0,-1))
c2=matrix(c(1,0,-1,0))
c4th=c1 - c2
All cognitive models used normalized scores as the dependent variable. Baseline cognitive performance was included as a covariate. No other covariates were included.
modelRVLT_imm_adj<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + RVLT_Immediate_Normedbaseline + (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_imm_adj)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## RVLT_Immediate_Normed ~ timefactor * Pandemic + RVLT_Immediate_Normedbaseline +
## (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 115453.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5846 -0.5593 -0.0308 0.5271 3.9457
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.40 2.098
## Residual 7.56 2.750
## Number of obs: 21992, groups: ID, 11325
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 7.224e+00 8.713e-02
## timefactor2 2.885e-01 5.551e-02
## PandemicFU2 data collected before COVID-19 1.291e-01 6.600e-02
## RVLT_Immediate_Normedbaseline 3.542e-01 7.141e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -7.246e-02 7.493e-02
## df t value
## (Intercept) 1.368e+04 82.911
## timefactor2 1.109e+04 5.197
## PandemicFU2 data collected before COVID-19 1.958e+04 1.956
## RVLT_Immediate_Normedbaseline 1.127e+04 49.598
## timefactor2:PandemicFU2 data collected before COVID-19 1.102e+04 -0.967
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 2.06e-07 ***
## PandemicFU2 data collected before COVID-19 0.0505 .
## RVLT_Immediate_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.3336
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tmfct2 PFdcbC RVLT_I
## timefactor2 -0.312
## PFU2dcbCOVI -0.359 0.411
## RVLT_Immd_N -0.830 0.001 -0.062
## t2:PFU2dcbC 0.232 -0.741 -0.561 -0.001
lsmeans(modelRVLT_imm_adj, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.92317 0.04860791 Inf 10.82790 11.01844
## FU2 data collected before COVID-19 11.05226 0.04454865 Inf 10.96495 11.13957
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.21165 0.04929225 Inf 11.11504 11.30826
## FU2 data collected before COVID-19 11.26829 0.04455099 Inf 11.18097 11.35561
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_adj, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.12909271 0.06599630 Inf -1.956 0.0505
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.05663759 0.06650667 Inf -0.852 0.3944
##
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_imm_adj, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.12909271 0.06599630 Inf -0.2584431 0.00025766
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.05663759 0.06650667 Inf -0.1869883 0.07371308
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTimmediate_lsmeans_adj <- summary(lsmeans(modelRVLT_imm_adj, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTimmediate_lsmeans_adj$Time<-NA
RVLTimmediate_lsmeans_adj$Time[RVLTimmediate_lsmeans_adj$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj$Time[RVLTimmediate_lsmeans_adj$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
lsmeans.RVLTImm2 <- lsmeans(modelRVLT_imm_adj, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21992' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21992)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.RVLTImm2,list(c4th),by=NULL)
## contrast estimate SE df z.ratio
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.07245512 0.07493437 Inf 0.967
## p.value
## 0.3336
##
## Degrees-of-freedom method: asymptotic
modelRVLT_del_adj<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic + RVLT_Delayed_Normedbaseline + (1|ID), data= Tracking.data_long_2)
summary(modelRVLT_del_adj)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## RVLT_Delayed_Normed ~ timefactor * Pandemic + RVLT_Delayed_Normedbaseline +
## (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 113063.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8852 -0.5489 -0.0326 0.5078 4.1922
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.283 2.069
## Residual 7.004 2.646
## Number of obs: 21795, groups: ID, 11307
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 6.795e+00 8.862e-02
## timefactor2 5.700e-01 5.382e-02
## PandemicFU2 data collected before COVID-19 2.406e-01 6.434e-02
## RVLT_Delayed_Normedbaseline 3.988e-01 7.162e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -2.737e-01 7.258e-02
## df t value
## (Intercept) 1.341e+04 76.674
## timefactor2 1.101e+04 10.591
## PandemicFU2 data collected before COVID-19 1.937e+04 3.740
## RVLT_Delayed_Normedbaseline 1.123e+04 55.680
## timefactor2:PandemicFU2 data collected before COVID-19 1.093e+04 -3.771
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 0.000185 ***
## RVLT_Delayed_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.000164 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tmfct2 PFdcbC RVLT_D
## timefactor2 -0.298
## PFU2dcbCOVI -0.348 0.409
## RVLT_Dlyd_N -0.845 0.001 -0.054
## t2:PFU2dcbC 0.221 -0.741 -0.557 0.000
lsmeans(modelRVLT_del_adj, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.07067 0.04742186 Inf 10.97773 11.16362
## FU2 data collected before COVID-19 11.31130 0.04342153 Inf 11.22620 11.39641
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.64071 0.04808827 Inf 11.54646 11.73496
## FU2 data collected before COVID-19 11.60764 0.04340093 Inf 11.52258 11.69271
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_adj, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.24062820 0.06434465 Inf -3.740 0.0002
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.03306667 0.06482383 Inf 0.510 0.6100
##
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_del_adj, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.24062820 0.06434465 Inf -0.3667414 -0.1145150
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.03306667 0.06482383 Inf -0.0939857 0.1601191
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTdelayed_lsmeans_adj <- summary(lsmeans(modelRVLT_del_adj, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTdelayed_lsmeans_adj$Time<-NA
RVLTdelayed_lsmeans_adj$Time[RVLTdelayed_lsmeans_adj$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj$Time[RVLTdelayed_lsmeans_adj$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
lsmeans.RVLTDel2 <- lsmeans(modelRVLT_del_adj, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21795' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21795)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.RVLTDel2,list(c4th),by=NULL)
## contrast estimate SE df z.ratio
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.2736949 0.0725854 Inf 3.771
## p.value
## 0.0002
##
## Degrees-of-freedom method: asymptotic
modelMAT_adj<- lmer(MAT_Normed~ timefactor*Pandemic + MAT_Normedbaseline + (1|ID), data= Tracking.data_long_2)
summary(modelMAT_adj)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + MAT_Normedbaseline + (1 |
## ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 107052.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3284 -0.5035 -0.0867 0.3301 4.9371
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.735 1.654
## Residual 8.485 2.913
## Number of obs: 20474, groups: ID, 11171
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 6.555e+00 8.903e-02
## timefactor2 -1.416e+00 6.142e-02
## PandemicFU2 data collected before COVID-19 -4.083e-01 6.619e-02
## MAT_Normedbaseline 4.523e-01 7.521e-03
## timefactor2:PandemicFU2 data collected before COVID-19 5.584e-01 8.278e-02
## df t value
## (Intercept) 1.371e+04 73.634
## timefactor2 1.053e+04 -23.048
## PandemicFU2 data collected before COVID-19 1.972e+04 -6.169
## MAT_Normedbaseline 1.096e+04 60.139
## timefactor2:PandemicFU2 data collected before COVID-19 1.044e+04 6.746
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 6.99e-10 ***
## MAT_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 1.60e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tmfct2 PFdcbC MAT_Nr
## timefactor2 -0.339
## PFU2dcbCOVI -0.385 0.457
## MAT_Nrmdbsl -0.836 -0.002 -0.026
## t2:PFU2dcbC 0.251 -0.742 -0.620 0.001
lsmeans(modelMAT_adj, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL
## FU2 data collected after COVID-19 11.091853 0.04892068 Inf 10.995971
## FU2 data collected before COVID-19 10.683509 0.04456865 Inf 10.596156
## asymp.UCL
## 11.187736
## 10.770862
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL
## FU2 data collected after COVID-19 9.676182 0.04948387 Inf 9.579195
## FU2 data collected before COVID-19 9.826236 0.04456114 Inf 9.738898
## asymp.UCL
## 9.773168
## 9.913574
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_adj, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.4083442 0.06618888 Inf 6.169 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1500545 0.06660025 Inf -2.253 0.0243
##
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelMAT_adj, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.4083442 0.06618888 Inf 0.2786164 0.5380720
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1500545 0.06660025 Inf -0.2805886 -0.0195204
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
MAT_lsmeans_adj <- summary(lsmeans(modelMAT_adj, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.
MAT_lsmeans_adj$Time<-NA
MAT_lsmeans_adj$Time[MAT_lsmeans_adj$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj$Time[MAT_lsmeans_adj$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
lsmeans.MAT2 <- lsmeans(modelMAT_adj, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20474' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20474)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.MAT2,list(c4th),by=NULL)
## contrast estimate SE df z.ratio
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.5583987 0.08277795 Inf -6.746
## p.value
## <.0001
##
## Degrees-of-freedom method: asymptotic
modelAnimals_adj<- lmer(Animal_Fluency_Normed~ timefactor*Pandemic + Animal_Fluency_Normedbaseline + (1|ID), data= Tracking.data_long_2)
summary(modelAnimals_adj)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Animal_Fluency_Normed ~ timefactor * Pandemic + Animal_Fluency_Normedbaseline +
## (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 98964.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8744 -0.5507 -0.0246 0.5316 4.6145
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.453 1.566
## Residual 3.204 1.790
## Number of obs: 22139, groups: ID, 11334
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 4.710e+00 7.132e-02
## timefactor2 6.205e-02 3.606e-02
## PandemicFU2 data collected before COVID-19 6.775e-02 4.516e-02
## Animal_Fluency_Normedbaseline 5.297e-01 6.190e-03
## timefactor2:PandemicFU2 data collected before COVID-19 3.004e-02 4.862e-02
## df t value
## (Intercept) 1.274e+04 66.044
## timefactor2 1.113e+04 1.720
## PandemicFU2 data collected before COVID-19 1.885e+04 1.500
## Animal_Fluency_Normedbaseline 1.126e+04 85.574
## timefactor2:PandemicFU2 data collected before COVID-19 1.106e+04 0.618
## Pr(>|t|)
## (Intercept) <2e-16 ***
## timefactor2 0.0854 .
## PandemicFU2 data collected before COVID-19 0.1335
## Animal_Fluency_Normedbaseline <2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.5367
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) tmfct2 PFdcbC An_F_N
## timefactor2 -0.245
## PFU2dcbCOVI -0.321 0.390
## Anml_Flnc_N -0.884 -0.001 -0.026
## t2:PFU2dcbC 0.182 -0.742 -0.531 0.001
lsmeans(modelAnimals_adj, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.16662 0.03330039 Inf 10.10136 10.23189
## FU2 data collected before COVID-19 10.23438 0.03048521 Inf 10.17463 10.29413
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.22867 0.03377601 Inf 10.16247 10.29487
## FU2 data collected before COVID-19 10.32647 0.03049575 Inf 10.26670 10.38624
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_adj, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.06775267 0.04515468 Inf -1.500 0.1335
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.09779421 0.04551335 Inf -2.149 0.0317
##
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelAnimals_adj, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.06775267 0.04515468 Inf -0.1562542 0.020748877
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.09779421 0.04551335 Inf -0.1869987 -0.008589689
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
Animals_lsmeans_adj <- summary(lsmeans(modelAnimals_adj, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.
Animals_lsmeans_adj$Time<-NA
Animals_lsmeans_adj$Time[Animals_lsmeans_adj$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj$Time[Animals_lsmeans_adj$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
lsmeans.Animals2 <- lsmeans(modelAnimals_adj, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 22139' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 22139)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.Animals2,list(c4th),by=NULL)
## contrast estimate SE df z.ratio
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.03004154 0.0486195 Inf -0.618
## p.value
## 0.5366
##
## Degrees-of-freedom method: asymptotic
All cognitive models used normalized scores. Each model is adjusted for age, sex, education level, ethnicity, and income level.
emm_options(rg.limit = 150000)
modelRVLT_imm_7<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel +
(1|ID), data= Tracking.data_long)
summary(modelRVLT_imm_7)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 171754.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6061 -0.5778 -0.0456 0.5216 5.0392
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.296 2.301
## Residual 8.493 2.914
## Number of obs: 32211, groups: ID, 10961
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 9.050e+00 2.499e-01
## timefactor2 6.938e-01 5.859e-02
## timefactor3 1.005e+00 5.921e-02
## PandemicFU2 data collected before COVID-19 5.404e-01 7.214e-02
## Age 7.354e-04 2.869e-03
## SexM -6.536e-01 5.846e-02
## EducationHigh School Diploma 1.772e-01 8.490e-02
## EducationLess than High School Diploma 6.349e-01 1.166e-01
## EducationSome College 2.389e-01 1.068e-01
## EthnicityWhite 8.362e-01 1.679e-01
## IncomeLevel>$150k 1.198e+00 1.611e-01
## IncomeLevel$100-150k 9.262e-01 1.274e-01
## IncomeLevel$20-50k 2.562e-01 8.253e-02
## IncomeLevel$50-100k 7.624e-01 8.893e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -2.254e-01 7.961e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -3.217e-01 8.007e-02
## df t value
## (Intercept) 1.130e+04 36.207
## timefactor2 2.134e+04 11.842
## timefactor3 2.147e+04 16.980
## PandemicFU2 data collected before COVID-19 2.449e+04 7.491
## Age 1.091e+04 0.256
## SexM 1.091e+04 -11.179
## EducationHigh School Diploma 1.092e+04 2.087
## EducationLess than High School Diploma 1.099e+04 5.444
## EducationSome College 1.087e+04 2.236
## EthnicityWhite 1.090e+04 4.981
## IncomeLevel>$150k 1.092e+04 7.435
## IncomeLevel$100-150k 1.088e+04 7.271
## IncomeLevel$20-50k 1.092e+04 3.104
## IncomeLevel$50-100k 1.091e+04 8.574
## timefactor2:PandemicFU2 data collected before COVID-19 2.134e+04 -2.831
## timefactor3:PandemicFU2 data collected before COVID-19 2.141e+04 -4.017
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## timefactor3 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 7.08e-14 ***
## Age 0.79771
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.03692 *
## EducationLess than High School Diploma 5.32e-08 ***
## EducationSome College 0.02538 *
## EthnicityWhite 6.42e-07 ***
## IncomeLevel>$150k 1.12e-13 ***
## IncomeLevel$100-150k 3.82e-13 ***
## IncomeLevel$20-50k 0.00191 **
## IncomeLevel$50-100k < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.00465 **
## timefactor3:PandemicFU2 data collected before COVID-19 5.91e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_imm_7, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.07777 0.1047309 Inf 9.872503 10.28304
## FU2 data collected before COVID-19 10.61817 0.1051213 Inf 10.412134 10.82420
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.77155 0.1049628 Inf 10.565822 10.97727
## FU2 data collected before COVID-19 11.08658 0.1053792 Inf 10.880041 11.29312
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.08318 0.1053072 Inf 10.876779 11.28957
## FU2 data collected before COVID-19 11.30192 0.1053620 Inf 11.095410 11.50842
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_7, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.5403959 0.07214149 Inf -7.491 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3150343 0.07279314 Inf -4.328 <.0001
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2187384 0.07331256 Inf -2.984 0.0028
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_imm_7, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.5403959 0.07214149 Inf -0.6817907 -0.3990012
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3150343 0.07279314 Inf -0.4577062 -0.1723623
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2187384 0.07331256 Inf -0.3624284 -0.0750485
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTimmediate_lsmeans_7 <- summary(lsmeans(modelRVLT_imm_7, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTimmediate_lsmeans_7$Time<-NA
RVLTimmediate_lsmeans_7$Time[RVLTimmediate_lsmeans_7$timefactor==1]<-"Baseline"
RVLTimmediate_lsmeans_7$Time[RVLTimmediate_lsmeans_7$timefactor==2]<-"Follow-up 1"
RVLTimmediate_lsmeans_7$Time[RVLTimmediate_lsmeans_7$timefactor==3]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_7, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from Baseline to FU2 by Pandemic status") +
theme_bw()
lsmeans.RVLTImm3 <- lsmeans(modelRVLT_imm_7, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32211' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32211)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.RVLTImm3,list(c1st,c2nd,c3rd),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.2253617 0.07961422 Inf
## structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) 0.0962958 0.08064700 Inf
## structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) 0.3216575 0.08007053 Inf
## z.ratio p.value
## 2.831 0.0046
## 1.194 0.2325
## 4.017 0.0001
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
modelRVLT_del_7<- lmer(RVLT_Delayed_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel +
(1|ID), data= Tracking.data_long)
summary(modelRVLT_del_7)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 168972.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8393 -0.5606 -0.0391 0.5104 5.0697
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.642 2.375
## Residual 7.751 2.784
## Number of obs: 32024, groups: ID, 10961
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 8.698e+00 2.517e-01
## timefactor2 5.258e-01 5.618e-02
## timefactor3 1.110e+00 5.677e-02
## PandemicFU2 data collected before COVID-19 4.403e-01 7.114e-02
## Age 1.075e-02 2.890e-03
## SexM -6.076e-01 5.887e-02
## EducationHigh School Diploma 3.403e-01 8.548e-02
## EducationLess than High School Diploma 4.722e-01 1.176e-01
## EducationSome College 2.878e-01 1.075e-01
## EthnicityWhite 8.402e-01 1.694e-01
## IncomeLevel>$150k 1.011e+00 1.622e-01
## IncomeLevel$100-150k 9.228e-01 1.283e-01
## IncomeLevel$20-50k 3.182e-01 8.314e-02
## IncomeLevel$50-100k 7.758e-01 8.956e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -3.515e-02 7.631e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -3.266e-01 7.673e-02
## df t value
## (Intercept) 1.132e+04 34.550
## timefactor2 2.120e+04 9.360
## timefactor3 2.132e+04 19.550
## PandemicFU2 data collected before COVID-19 2.333e+04 6.189
## Age 1.094e+04 3.718
## SexM 1.092e+04 -10.321
## EducationHigh School Diploma 1.093e+04 3.980
## EducationLess than High School Diploma 1.106e+04 4.014
## EducationSome College 1.087e+04 2.676
## EthnicityWhite 1.099e+04 4.960
## IncomeLevel>$150k 1.091e+04 6.232
## IncomeLevel$100-150k 1.090e+04 7.193
## IncomeLevel$20-50k 1.094e+04 3.827
## IncomeLevel$50-100k 1.092e+04 8.663
## timefactor2:PandemicFU2 data collected before COVID-19 2.120e+04 -0.461
## timefactor3:PandemicFU2 data collected before COVID-19 2.126e+04 -4.257
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## timefactor3 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 6.17e-10 ***
## Age 0.000202 ***
## SexM < 2e-16 ***
## EducationHigh School Diploma 6.92e-05 ***
## EducationLess than High School Diploma 6.01e-05 ***
## EducationSome College 0.007455 **
## EthnicityWhite 7.15e-07 ***
## IncomeLevel>$150k 4.78e-10 ***
## IncomeLevel$100-150k 6.73e-13 ***
## IncomeLevel$20-50k 0.000130 ***
## IncomeLevel$50-100k < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.645107
## timefactor3:PandemicFU2 data collected before COVID-19 2.08e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_del_7, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.35532 0.1050081 Inf 10.14951 10.56113
## FU2 data collected before COVID-19 10.79559 0.1054942 Inf 10.58882 11.00235
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.88113 0.1053799 Inf 10.67459 11.08767
## FU2 data collected before COVID-19 11.28625 0.1058256 Inf 11.07883 11.49366
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.46514 0.1056618 Inf 11.25805 11.67223
## FU2 data collected before COVID-19 11.57880 0.1057992 Inf 11.37144 11.78617
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_7, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4402639 0.07114179 Inf -6.189 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4051153 0.07201606 Inf -5.625 <.0001
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1136644 0.07247556 Inf -1.568 0.1168
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_del_7, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4402639 0.07114179 Inf -0.5796993 -0.30082857
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4051153 0.07201606 Inf -0.5462642 -0.26396640
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1136644 0.07247556 Inf -0.2557139 0.02838505
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTdelayed_lsmeans_7 <- summary(lsmeans(modelRVLT_del_7, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTdelayed_lsmeans_7$Time<-NA
RVLTdelayed_lsmeans_7$Time[RVLTdelayed_lsmeans_7$timefactor==1]<-"Baseline"
RVLTdelayed_lsmeans_7$Time[RVLTdelayed_lsmeans_7$timefactor==2]<-"Follow-up 1"
RVLTdelayed_lsmeans_7$Time[RVLTdelayed_lsmeans_7$timefactor==3]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_7, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from Baseline to FU2 by Pandemic status") +
theme_bw()
lsmeans.RVLTDel3 <- lsmeans(modelRVLT_del_7, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32024' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32024)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.RVLTDel3,list(c1st,c2nd,c3rd),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.0351486 0.07631430 Inf
## structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) 0.2914509 0.07750896 Inf
## structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) 0.3265995 0.07672876 Inf
## z.ratio p.value
## 0.461 0.6451
## 3.760 0.0002
## 4.257 <.0001
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
modelMAT_7<- lmer(MAT_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel +
(1|ID), data= Tracking.data_long)
summary(modelMAT_7)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 161455.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1003 -0.4734 -0.0185 0.4253 4.8862
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.047 2.247
## Residual 7.684 2.772
## Number of obs: 30761, groups: ID, 10961
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 8.745e+00 2.449e-01
## timefactor2 1.159e+00 5.726e-02
## timefactor3 -2.456e-01 5.770e-02
## PandemicFU2 data collected before COVID-19 1.342e-01 6.935e-02
## Age -2.369e-03 2.813e-03
## SexM -9.652e-01 5.717e-02
## EducationHigh School Diploma -1.402e-02 8.303e-02
## EducationLess than High School Diploma -2.118e-01 1.147e-01
## EducationSome College -3.669e-02 1.042e-01
## EthnicityWhite 1.321e+00 1.646e-01
## IncomeLevel>$150k 8.113e-01 1.573e-01
## IncomeLevel$100-150k 1.000e+00 1.244e-01
## IncomeLevel$20-50k 3.765e-01 8.080e-02
## IncomeLevel$50-100k 7.398e-01 8.700e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -5.205e-01 7.763e-02
## timefactor3:PandemicFU2 data collected before COVID-19 1.626e-02 7.794e-02
## df t value
## (Intercept) 1.130e+04 35.715
## timefactor2 2.026e+04 20.236
## timefactor3 2.034e+04 -4.257
## PandemicFU2 data collected before COVID-19 2.332e+04 1.935
## Age 1.096e+04 -0.842
## SexM 1.085e+04 -16.882
## EducationHigh School Diploma 1.086e+04 -0.169
## EducationLess than High School Diploma 1.111e+04 -1.847
## EducationSome College 1.074e+04 -0.352
## EthnicityWhite 1.094e+04 8.026
## IncomeLevel>$150k 1.081e+04 5.157
## IncomeLevel$100-150k 1.080e+04 8.040
## IncomeLevel$20-50k 1.090e+04 4.660
## IncomeLevel$50-100k 1.086e+04 8.504
## timefactor2:PandemicFU2 data collected before COVID-19 2.023e+04 -6.705
## timefactor3:PandemicFU2 data collected before COVID-19 2.027e+04 0.209
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## timefactor3 2.09e-05 ***
## PandemicFU2 data collected before COVID-19 0.0530 .
## Age 0.3999
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.8659
## EducationLess than High School Diploma 0.0648 .
## EducationSome College 0.7248
## EthnicityWhite 1.11e-15 ***
## IncomeLevel>$150k 2.56e-07 ***
## IncomeLevel$100-150k 9.96e-16 ***
## IncomeLevel$20-50k 3.20e-06 ***
## IncomeLevel$50-100k < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 2.07e-11 ***
## timefactor3:PandemicFU2 data collected before COVID-19 0.8347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelMAT_7, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.297556 0.1020499 Inf 9.097542 9.497570
## FU2 data collected before COVID-19 9.431782 0.1025383 Inf 9.230810 9.632753
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.456213 0.1031868 Inf 10.253971 10.658456
## FU2 data collected before COVID-19 10.069931 0.1033760 Inf 9.867317 10.272544
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.051972 0.1033932 Inf 8.849325 9.254619
## FU2 data collected before COVID-19 9.202458 0.1033700 Inf 8.999857 9.405060
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_7, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1342255 0.06935486 Inf -1.935 0.0529
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.3862827 0.07206039 Inf 5.361 <.0001
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1504864 0.07239121 Inf -2.079 0.0376
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelMAT_7, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1342255 0.06935486 Inf -0.2701585 0.0017076
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.3862827 0.07206039 Inf 0.2450469 0.5275185
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1504864 0.07239121 Inf -0.2923706 -0.0086023
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
MAT_lsmeans_7 <- summary(lsmeans(modelMAT_7, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.
MAT_lsmeans_7$Time<-NA
MAT_lsmeans_7$Time[MAT_lsmeans_7$timefactor==1]<-"Baseline"
MAT_lsmeans_7$Time[MAT_lsmeans_7$timefactor==2]<-"Follow-up 1"
MAT_lsmeans_7$Time[MAT_lsmeans_7$timefactor==3]<-"Follow-up 2"
ggplot(MAT_lsmeans_7, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from Baseline to FU2 by Pandemic status") +
theme_bw()
lsmeans.MAT3 <- lsmeans(modelMAT_7, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 30761' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 30761)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.MAT3,list(c1st,c2nd,c3rd),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.5205082 0.07763266 Inf
## structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -0.5367691 0.08021640 Inf
## structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -0.0162610 0.07793864 Inf
## z.ratio p.value
## 6.705 <.0001
## -6.692 <.0001
## -0.209 0.8347
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
modelAnimals_7<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel +
(1|ID), data= Tracking.data_long)
summary(modelAnimals_7)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 151298.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3310 -0.5632 -0.0182 0.5489 4.7037
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.916 2.217
## Residual 3.651 1.911
## Number of obs: 32347, groups: ID, 10961
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 9.139e+00 2.158e-01
## timefactor2 -7.973e-02 3.837e-02
## timefactor3 -1.972e-02 3.882e-02
## PandemicFU2 data collected before COVID-19 2.106e-01 5.702e-02
## Age -9.285e-03 2.486e-03
## SexM -2.783e-01 5.066e-02
## EducationHigh School Diploma 3.197e-01 7.357e-02
## EducationLess than High School Diploma 4.108e-01 1.009e-01
## EducationSome College 2.551e-01 9.261e-02
## EthnicityWhite 1.397e+00 1.454e-01
## IncomeLevel>$150k 7.018e-01 1.396e-01
## IncomeLevel$100-150k 6.896e-01 1.104e-01
## IncomeLevel$20-50k 1.830e-01 7.151e-02
## IncomeLevel$50-100k 4.821e-01 7.706e-02
## timefactor2:PandemicFU2 data collected before COVID-19 -2.362e-02 5.211e-02
## timefactor3:PandemicFU2 data collected before COVID-19 3.057e-03 5.245e-02
## df t value
## (Intercept) 1.117e+04 42.351
## timefactor2 2.144e+04 -2.078
## timefactor3 2.153e+04 -0.508
## PandemicFU2 data collected before COVID-19 1.928e+04 3.693
## Age 1.095e+04 -3.735
## SexM 1.094e+04 -5.492
## EducationHigh School Diploma 1.096e+04 4.345
## EducationLess than High School Diploma 1.098e+04 4.070
## EducationSome College 1.092e+04 2.755
## EthnicityWhite 1.094e+04 9.607
## IncomeLevel>$150k 1.095e+04 5.029
## IncomeLevel$100-150k 1.094e+04 6.244
## IncomeLevel$20-50k 1.095e+04 2.559
## IncomeLevel$50-100k 1.094e+04 6.256
## timefactor2:PandemicFU2 data collected before COVID-19 2.144e+04 -0.453
## timefactor3:PandemicFU2 data collected before COVID-19 2.149e+04 0.058
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 0.037722 *
## timefactor3 0.611443
## PandemicFU2 data collected before COVID-19 0.000222 ***
## Age 0.000189 ***
## SexM 4.05e-08 ***
## EducationHigh School Diploma 1.41e-05 ***
## EducationLess than High School Diploma 4.74e-05 ***
## EducationSome College 0.005879 **
## EthnicityWhite < 2e-16 ***
## IncomeLevel>$150k 5.02e-07 ***
## IncomeLevel$100-150k 4.43e-10 ***
## IncomeLevel$20-50k 0.010512 *
## IncomeLevel$50-100k 4.11e-10 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.650419
## timefactor3:PandemicFU2 data collected before COVID-19 0.953525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelAnimals_7, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.784883 0.08876053 Inf 9.610915 9.958850
## FU2 data collected before COVID-19 9.995461 0.08941223 Inf 9.820216 10.170705
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.705153 0.08886856 Inf 9.530974 9.879332
## FU2 data collected before COVID-19 9.892114 0.08949478 Inf 9.716707 10.067520
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.765159 0.08904435 Inf 9.590635 9.939683
## FU2 data collected before COVID-19 9.978794 0.08951106 Inf 9.803356 10.154233
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_7, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2105781 0.05702043 Inf -3.693 0.0002
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1869606 0.05729576 Inf -3.263 0.0011
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2136350 0.05761450 Inf -3.708 0.0002
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelAnimals_7, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2105781 0.05702043 Inf -0.3223361 -0.09882015
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1869606 0.05729576 Inf -0.2992583 -0.07466299
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2136350 0.05761450 Inf -0.3265574 -0.10071268
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
Animals_lsmeans_7 <- summary(lsmeans(modelAnimals_7, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.
Animals_lsmeans_7$Time<-NA
Animals_lsmeans_7$Time[Animals_lsmeans_7$timefactor==1]<-"Baseline"
Animals_lsmeans_7$Time[Animals_lsmeans_7$timefactor==2]<-"Follow-up 1"
Animals_lsmeans_7$Time[Animals_lsmeans_7$timefactor==3]<-"Follow-up 2"
ggplot(Animals_lsmeans_7, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from Baseline to FU2 by Pandemic status") +
theme_bw()
lsmeans.Animals3 <- lsmeans(modelAnimals_7, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 32347' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 32347)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.Animals3,list(c1st,c2nd,c3rd),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.02361753 0.05211439 Inf
## structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -0.02667440 0.05273844 Inf
## structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -0.00305687 0.05245070 Inf
## z.ratio p.value
## 0.453 0.6504
## -0.506 0.6130
## -0.058 0.9535
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
All models adjusted for baseline, age, sex, education level, ethnicity, income level
modelRVLT_imm_adj3<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + RVLT_Immediate_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelRVLT_imm_adj3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + RVLT_Immediate_Normedbaseline + (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 111271.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6415 -0.5658 -0.0343 0.5293 3.8307
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.176 2.043
## Residual 7.553 2.748
## Number of obs: 21250, groups: ID, 10936
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 8.194e+00 2.533e-01
## timefactor2 3.090e-01 5.631e-02
## PandemicFU2 data collected before COVID-19 1.905e-01 6.742e-02
## Age -2.881e-02 2.866e-03
## SexM -4.640e-01 5.834e-02
## EducationHigh School Diploma 1.805e-01 8.451e-02
## EducationLess than High School Diploma 4.577e-01 1.164e-01
## EducationSome College 1.508e-01 1.062e-01
## EthnicityWhite 5.942e-01 1.672e-01
## IncomeLevel>$150k 7.517e-01 1.606e-01
## IncomeLevel$100-150k 6.567e-01 1.268e-01
## IncomeLevel$20-50k 2.418e-01 8.216e-02
## IncomeLevel$50-100k 5.780e-01 8.861e-02
## RVLT_Immediate_Normedbaseline 3.545e-01 7.244e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -9.245e-02 7.615e-02
## df t value
## (Intercept) 1.109e+04 32.342
## timefactor2 1.072e+04 5.487
## PandemicFU2 data collected before COVID-19 1.882e+04 2.826
## Age 1.088e+04 -10.051
## SexM 1.086e+04 -7.954
## EducationHigh School Diploma 1.088e+04 2.136
## EducationLess than High School Diploma 1.101e+04 3.932
## EducationSome College 1.078e+04 1.420
## EthnicityWhite 1.081e+04 3.554
## IncomeLevel>$150k 1.085e+04 4.679
## IncomeLevel$100-150k 1.082e+04 5.179
## IncomeLevel$20-50k 1.088e+04 2.943
## IncomeLevel$50-100k 1.086e+04 6.523
## RVLT_Immediate_Normedbaseline 1.088e+04 48.929
## timefactor2:PandemicFU2 data collected before COVID-19 1.066e+04 -1.214
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 4.18e-08 ***
## PandemicFU2 data collected before COVID-19 0.00472 **
## Age < 2e-16 ***
## SexM 1.98e-15 ***
## EducationHigh School Diploma 0.03273 *
## EducationLess than High School Diploma 8.48e-05 ***
## EducationSome College 0.15567
## EthnicityWhite 0.00038 ***
## IncomeLevel>$150k 2.91e-06 ***
## IncomeLevel$100-150k 2.27e-07 ***
## IncomeLevel$20-50k 0.00326 **
## IncomeLevel$50-100k 7.21e-11 ***
## RVLT_Immediate_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.22472
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_imm_adj3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.83015 0.1026192 Inf 10.62902 11.03128
## FU2 data collected before COVID-19 11.02067 0.1033453 Inf 10.81812 11.22322
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.13912 0.1029498 Inf 10.93735 11.34090
## FU2 data collected before COVID-19 11.23719 0.1033229 Inf 11.03468 11.43970
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_adj3, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.19052148 0.06742308 Inf -2.826 0.0047
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.09806713 0.06796828 Inf -1.443 0.1491
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_imm_adj3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.19052148 0.06742308 Inf -0.3226683 -0.05837468
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.09806713 0.06796828 Inf -0.2312825 0.03514825
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTimmediate_lsmeans_adj3 <- summary(lsmeans(modelRVLT_imm_adj3, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTimmediate_lsmeans_adj3$Time<-NA
RVLTimmediate_lsmeans_adj3$Time[RVLTimmediate_lsmeans_adj3$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj3$Time[RVLTimmediate_lsmeans_adj3$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj3, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
lsmeans.RVLTImm4 <- lsmeans(modelRVLT_imm_adj3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21250' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21250)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.RVLTImm4,list(c4th),by=NULL)
## contrast estimate SE df z.ratio
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.09245435 0.07614784 Inf 1.214
## p.value
## 0.2247
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
modelRVLT_del_adj3<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + RVLT_Delayed_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelRVLT_del_adj3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + RVLT_Delayed_Normedbaseline + (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 109120.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9429 -0.5473 -0.0380 0.5072 4.1729
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.160 2.040
## Residual 7.003 2.646
## Number of obs: 21063, groups: ID, 10918
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 7.067e+00 2.505e-01
## timefactor2 5.822e-01 5.461e-02
## PandemicFU2 data collected before COVID-19 2.847e-01 6.604e-02
## Age -1.943e-02 2.828e-03
## SexM -4.248e-01 5.741e-02
## EducationHigh School Diploma 2.504e-01 8.320e-02
## EducationLess than High School Diploma 2.394e-01 1.151e-01
## EducationSome College 1.736e-01 1.045e-01
## EthnicityWhite 7.156e-01 1.653e-01
## IncomeLevel>$150k 6.427e-01 1.579e-01
## IncomeLevel$100-150k 5.286e-01 1.249e-01
## IncomeLevel$20-50k 2.425e-01 8.096e-02
## IncomeLevel$50-100k 6.082e-01 8.723e-02
## RVLT_Delayed_Normedbaseline 3.992e-01 7.311e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -2.896e-01 7.378e-02
## df t value
## (Intercept) 1.115e+04 28.209
## timefactor2 1.065e+04 10.661
## PandemicFU2 data collected before COVID-19 1.856e+04 4.310
## Age 1.090e+04 -6.869
## SexM 1.085e+04 -7.400
## EducationHigh School Diploma 1.085e+04 3.010
## EducationLess than High School Diploma 1.108e+04 2.080
## EducationSome College 1.075e+04 1.662
## EthnicityWhite 1.092e+04 4.328
## IncomeLevel>$150k 1.083e+04 4.071
## IncomeLevel$100-150k 1.082e+04 4.234
## IncomeLevel$20-50k 1.089e+04 2.995
## IncomeLevel$50-100k 1.085e+04 6.972
## RVLT_Delayed_Normedbaseline 1.084e+04 54.595
## timefactor2:PandemicFU2 data collected before COVID-19 1.058e+04 -3.925
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 1.64e-05 ***
## Age 6.84e-12 ***
## SexM 1.46e-13 ***
## EducationHigh School Diploma 0.00262 **
## EducationLess than High School Diploma 0.03751 *
## EducationSome College 0.09657 .
## EthnicityWhite 1.52e-05 ***
## IncomeLevel>$150k 4.71e-05 ***
## IncomeLevel$100-150k 2.32e-05 ***
## IncomeLevel$20-50k 0.00275 **
## IncomeLevel$50-100k 3.31e-12 ***
## RVLT_Delayed_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 8.72e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_del_adj3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.86661 0.1012336 Inf 10.66820 11.06503
## FU2 data collected before COVID-19 11.15128 0.1019189 Inf 10.95152 11.35103
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.44886 0.1014974 Inf 11.24993 11.64779
## FU2 data collected before COVID-19 11.44389 0.1018863 Inf 11.24420 11.64359
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_adj3, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.28466347 0.06604078 Inf -4.310 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.00496514 0.06653229 Inf 0.075 0.9405
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_del_adj3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.28466347 0.06604078 Inf -0.4141010 -0.1552259
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.00496514 0.06653229 Inf -0.1254358 0.1353660
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTdelayed_lsmeans_adj3 <- summary(lsmeans(modelRVLT_del_adj3, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTdelayed_lsmeans_adj3$Time<-NA
RVLTdelayed_lsmeans_adj3$Time[RVLTdelayed_lsmeans_adj3$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj3$Time[RVLTdelayed_lsmeans_adj3$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj3, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
lsmeans.RVLTDel4 <- lsmeans(modelRVLT_del_adj3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21063' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21063)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.RVLTDel4,list(c4th),by=NULL)
## contrast estimate SE df z.ratio
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.2896286 0.07378406 Inf 3.925
## p.value
## 0.0001
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
modelMAT_adj3<- lmer(MAT_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + MAT_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelMAT_adj3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + MAT_Normedbaseline + (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 102961.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4724 -0.5194 -0.0813 0.3623 4.8819
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.395 1.548
## Residual 8.438 2.905
## Number of obs: 19800, groups: ID, 10792
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 7.143e+00 2.420e-01
## timefactor2 -1.408e+00 6.210e-02
## PandemicFU2 data collected before COVID-19 -5.115e-01 6.711e-02
## Age -1.415e-02 2.710e-03
## SexM -1.122e+00 5.481e-02
## EducationHigh School Diploma -6.998e-02 7.956e-02
## EducationLess than High School Diploma -2.842e-01 1.114e-01
## EducationSome College -1.918e-01 9.920e-02
## EthnicityWhite 8.658e-01 1.585e-01
## IncomeLevel>$150k 1.143e-01 1.506e-01
## IncomeLevel$100-150k 1.672e-01 1.193e-01
## IncomeLevel$20-50k 1.217e-01 7.764e-02
## IncomeLevel$50-100k 1.579e-01 8.366e-02
## MAT_Normedbaseline 4.487e-01 7.518e-03
## timefactor2:PandemicFU2 data collected before COVID-19 5.431e-01 8.382e-02
## df t value
## (Intercept) 1.091e+04 29.518
## timefactor2 1.022e+04 -22.677
## PandemicFU2 data collected before COVID-19 1.904e+04 -7.622
## Age 1.069e+04 -5.221
## SexM 1.052e+04 -20.466
## EducationHigh School Diploma 1.050e+04 -0.880
## EducationLess than High School Diploma 1.081e+04 -2.551
## EducationSome College 1.035e+04 -1.934
## EthnicityWhite 1.066e+04 5.462
## IncomeLevel>$150k 1.044e+04 0.759
## IncomeLevel$100-150k 1.046e+04 1.402
## IncomeLevel$20-50k 1.058e+04 1.567
## IncomeLevel$50-100k 1.051e+04 1.887
## MAT_Normedbaseline 1.057e+04 59.680
## timefactor2:PandemicFU2 data collected before COVID-19 1.013e+04 6.480
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 2.61e-14 ***
## Age 1.81e-07 ***
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.3791
## EducationLess than High School Diploma 0.0107 *
## EducationSome College 0.0532 .
## EthnicityWhite 4.81e-08 ***
## IncomeLevel>$150k 0.4480
## IncomeLevel$100-150k 0.1610
## IncomeLevel$20-50k 0.1171
## IncomeLevel$50-100k 0.0591 .
## MAT_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 9.58e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelMAT_adj3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL
## FU2 data collected after COVID-19 10.628611 0.09840109 Inf 10.435748
## FU2 data collected before COVID-19 10.117090 0.09865595 Inf 9.923728
## asymp.UCL
## 10.821473
## 10.310452
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL
## FU2 data collected after COVID-19 9.220413 0.09860619 Inf 9.027148
## FU2 data collected before COVID-19 9.252042 0.09865478 Inf 9.058682
## asymp.UCL
## 9.413678
## 9.445402
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_adj3, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.5115203 0.06710904 Inf 7.622 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0316291 0.06748026 Inf -0.469 0.6393
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelMAT_adj3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.5115203 0.06710904 Inf 0.379989 0.6430516
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0316291 0.06748026 Inf -0.163888 0.1006298
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
MAT_lsmeans_adj3 <- summary(lsmeans(modelMAT_adj3, ~Pandemic|timefactor))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.
MAT_lsmeans_adj3$Time<-NA
MAT_lsmeans_adj3$Time[MAT_lsmeans_adj3$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj3$Time[MAT_lsmeans_adj3$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj3, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
lsmeans.MAT4 <- lsmeans(modelMAT_adj3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19800' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19800)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.MAT4,list(c4th),by=NULL)
## contrast estimate SE df z.ratio
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.5431494 0.08381608 Inf -6.480
## p.value
## <.0001
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
modelAnimals_adj3<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + Animal_Fluency_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelAnimals_adj3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + Animal_Fluency_Normedbaseline + (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 95445.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9116 -0.5510 -0.0211 0.5305 4.7530
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.387 1.545
## Residual 3.202 1.789
## Number of obs: 21386, groups: ID, 10943
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 5.335e+00 1.823e-01
## timefactor2 5.965e-02 3.660e-02
## PandemicFU2 data collected before COVID-19 1.088e-01 4.639e-02
## Age -2.011e-02 2.016e-03
## SexM -1.487e-01 4.110e-02
## EducationHigh School Diploma 2.225e-01 5.973e-02
## EducationLess than High School Diploma 1.072e-01 8.206e-02
## EducationSome College 1.838e-01 7.499e-02
## EthnicityWhite 6.034e-01 1.183e-01
## IncomeLevel>$150k 1.189e-01 1.133e-01
## IncomeLevel$100-150k 1.489e-01 8.968e-02
## IncomeLevel$20-50k 1.515e-03 5.802e-02
## IncomeLevel$50-100k 1.656e-01 6.256e-02
## Animal_Fluency_Normedbaseline 5.260e-01 6.297e-03
## timefactor2:PandemicFU2 data collected before COVID-19 2.642e-02 4.942e-02
## df t value
## (Intercept) 1.108e+04 29.261
## timefactor2 1.076e+04 1.630
## PandemicFU2 data collected before COVID-19 1.803e+04 2.346
## Age 1.089e+04 -9.975
## SexM 1.087e+04 -3.617
## EducationHigh School Diploma 1.089e+04 3.725
## EducationLess than High School Diploma 1.098e+04 1.307
## EducationSome College 1.082e+04 2.451
## EthnicityWhite 1.085e+04 5.103
## IncomeLevel>$150k 1.087e+04 1.049
## IncomeLevel$100-150k 1.086e+04 1.660
## IncomeLevel$20-50k 1.088e+04 0.026
## IncomeLevel$50-100k 1.087e+04 2.647
## Animal_Fluency_Normedbaseline 1.086e+04 83.529
## timefactor2:PandemicFU2 data collected before COVID-19 1.068e+04 0.535
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 0.103178
## PandemicFU2 data collected before COVID-19 0.018968 *
## Age < 2e-16 ***
## SexM 0.000300 ***
## EducationHigh School Diploma 0.000196 ***
## EducationLess than High School Diploma 0.191407
## EducationSome College 0.014258 *
## EthnicityWhite 3.4e-07 ***
## IncomeLevel>$150k 0.294009
## IncomeLevel$100-150k 0.096843 .
## IncomeLevel$20-50k 0.979174
## IncomeLevel$50-100k 0.008135 **
## Animal_Fluency_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.592928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 15 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelAnimals_adj3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL
## FU2 data collected after COVID-19 9.964733 0.07208383 Inf 9.823451
## FU2 data collected before COVID-19 10.073574 0.07255307 Inf 9.931373
## asymp.UCL
## 10.10601
## 10.21578
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL
## FU2 data collected after COVID-19 10.024383 0.07226703 Inf 9.882742
## FU2 data collected before COVID-19 10.159647 0.07258012 Inf 10.017393
## asymp.UCL
## 10.16602
## 10.30190
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_adj3, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1088415 0.04638720 Inf -2.346 0.0190
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1352646 0.04675466 Inf -2.893 0.0038
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelAnimals_adj3, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1088415 0.04638720 Inf -0.1997587 -0.01792425
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1352646 0.04675466 Inf -0.2269021 -0.04362719
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
Animals_lsmeans_adj3 <- summary(lsmeans(modelAnimals_adj3, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.
Animals_lsmeans_adj3$Time<-NA
Animals_lsmeans_adj3$Time[Animals_lsmeans_adj3$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj3$Time[Animals_lsmeans_adj3$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj3, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
lsmeans.Animals4 <- lsmeans(modelAnimals_adj3, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 21386' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 21386)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.Animals4,list(c4th),by=NULL)
## contrast estimate SE df z.ratio
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.02642315 0.04942468 Inf -0.535
## p.value
## 0.5929
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel
## Degrees-of-freedom method: asymptotic
All models use normalized cognitive scores.Each model is adjusted for baseline age, sex, education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, and baseline PASE score
emm_options(rg.limit = 175000)
modelRVLT_imm_8<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= Tracking.data_long)
summary(modelRVLT_imm_8)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 132322.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6230 -0.5804 -0.0448 0.5230 5.1001
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.227 2.286
## Residual 8.338 2.888
## Number of obs: 24887, groups: ID, 8467
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 8.092e+00 4.293e-01
## timefactor2 7.000e-01 6.641e-02
## timefactor3 1.013e+00 6.709e-02
## PandemicFU2 data collected before COVID-19 5.421e-01 8.161e-02
## Age 1.697e-02 4.072e-03
## SexM -7.871e-01 6.955e-02
## EducationHigh School Diploma 2.312e-01 9.790e-02
## EducationLess than High School Diploma 7.346e-01 1.383e-01
## EducationSome College 3.476e-01 1.218e-01
## EthnicityWhite 9.632e-01 1.941e-01
## IncomeLevel>$150k 8.978e-01 1.819e-01
## IncomeLevel$100-150k 5.815e-01 1.453e-01
## IncomeLevel$20-50k 8.931e-02 9.675e-02
## IncomeLevel$50-100k 5.424e-01 1.042e-01
## BMI -1.859e-02 6.571e-03
## CESD.20.1 -3.552e-02 7.818e-03
## SmokingStatusFormer Smoker 1.920e-01 1.334e-01
## SmokingStatusNever Smoked 3.584e-01 1.388e-01
## SmokingStatusOccasional Smoker 2.849e-01 2.628e-01
## RelationshipstatusMarried 1.057e-01 1.137e-01
## RelationshipstatusSeparated -1.018e-01 2.239e-01
## RelationshipstatusSingle -7.809e-02 1.536e-01
## RelationshipstatusWidowed -1.043e-01 1.553e-01
## LivingstatusAssisted Living -6.438e-01 4.609e-01
## LivingstatusHouse 1.093e-01 1.024e-01
## LivingstatusOther -3.383e-01 3.822e-01
## AnxietyYes -1.932e-02 1.367e-01
## MoodDisordYes -1.291e-01 9.913e-02
## Chronicconditions -3.727e-02 1.602e-02
## PASE_TOTALbaseline 3.077e-03 4.664e-04
## timefactor2:PandemicFU2 data collected before COVID-19 -2.336e-01 8.985e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -3.202e-01 9.033e-02
## df t value
## (Intercept) 8.532e+03 18.849
## timefactor2 1.648e+04 10.541
## timefactor3 1.658e+04 15.104
## PandemicFU2 data collected before COVID-19 1.881e+04 6.642
## Age 8.410e+03 4.166
## SexM 8.405e+03 -11.316
## EducationHigh School Diploma 8.410e+03 2.361
## EducationLess than High School Diploma 8.469e+03 5.312
## EducationSome College 8.372e+03 2.854
## EthnicityWhite 8.367e+03 4.962
## IncomeLevel>$150k 8.408e+03 4.937
## IncomeLevel$100-150k 8.380e+03 4.001
## IncomeLevel$20-50k 8.412e+03 0.923
## IncomeLevel$50-100k 8.409e+03 5.204
## BMI 8.393e+03 -2.829
## CESD.20.1 8.403e+03 -4.543
## SmokingStatusFormer Smoker 8.409e+03 1.439
## SmokingStatusNever Smoked 8.409e+03 2.582
## SmokingStatusOccasional Smoker 8.357e+03 1.084
## RelationshipstatusMarried 8.417e+03 0.930
## RelationshipstatusSeparated 8.448e+03 -0.455
## RelationshipstatusSingle 8.410e+03 -0.508
## RelationshipstatusWidowed 8.423e+03 -0.672
## LivingstatusAssisted Living 8.432e+03 -1.397
## LivingstatusHouse 8.412e+03 1.067
## LivingstatusOther 8.349e+03 -0.885
## AnxietyYes 8.402e+03 -0.141
## MoodDisordYes 8.403e+03 -1.303
## Chronicconditions 8.397e+03 -2.326
## PASE_TOTALbaseline 8.401e+03 6.597
## timefactor2:PandemicFU2 data collected before COVID-19 1.649e+04 -2.600
## timefactor3:PandemicFU2 data collected before COVID-19 1.654e+04 -3.545
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## timefactor3 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 3.18e-11 ***
## Age 3.13e-05 ***
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.018232 *
## EducationLess than High School Diploma 1.11e-07 ***
## EducationSome College 0.004325 **
## EthnicityWhite 7.11e-07 ***
## IncomeLevel>$150k 8.10e-07 ***
## IncomeLevel$100-150k 6.35e-05 ***
## IncomeLevel$20-50k 0.355947
## IncomeLevel$50-100k 2.00e-07 ***
## BMI 0.004682 **
## CESD.20.1 5.63e-06 ***
## SmokingStatusFormer Smoker 0.150085
## SmokingStatusNever Smoked 0.009843 **
## SmokingStatusOccasional Smoker 0.278288
## RelationshipstatusMarried 0.352565
## RelationshipstatusSeparated 0.649467
## RelationshipstatusSingle 0.611151
## RelationshipstatusWidowed 0.501874
## LivingstatusAssisted Living 0.162528
## LivingstatusHouse 0.285969
## LivingstatusOther 0.376102
## AnxietyYes 0.887554
## MoodDisordYes 0.192732
## Chronicconditions 0.020038 *
## PASE_TOTALbaseline 4.46e-11 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.009323 **
## timefactor3:PandemicFU2 data collected before COVID-19 0.000393 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_imm_8, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.597492 0.2109562 Inf 9.184025 10.01096
## FU2 data collected before COVID-19 10.139580 0.2108650 Inf 9.726292 10.55287
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.297469 0.2110827 Inf 9.883755 10.71118
## FU2 data collected before COVID-19 10.605931 0.2110372 Inf 10.192306 11.01956
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.610743 0.2113088 Inf 10.196585 11.02490
## FU2 data collected before COVID-19 10.832585 0.2109641 Inf 10.419103 11.24607
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_8, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.5420881 0.08161399 Inf -6.642 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3084620 0.08236585 Inf -3.745 0.0002
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2218424 0.08290923 Inf -2.676 0.0075
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_imm_8, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.5420881 0.08161399 Inf -0.7020486 -0.3821276
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3084620 0.08236585 Inf -0.4698961 -0.1470279
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2218424 0.08290923 Inf -0.3843416 -0.0593433
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTimmediate_lsmeans_8 <- summary(lsmeans(modelRVLT_imm_8, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTimmediate_lsmeans_8$Time<-NA
RVLTimmediate_lsmeans_8$Time[RVLTimmediate_lsmeans_8$timefactor==1]<-"Baseline"
RVLTimmediate_lsmeans_8$Time[RVLTimmediate_lsmeans_8$timefactor==2]<-"Follow-up 1"
RVLTimmediate_lsmeans_8$Time[RVLTimmediate_lsmeans_8$timefactor==3]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_8, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "RVLT Immediate Score", title = "RVLT Immediate Score from Baseline to FU2 by Pandemic status") +
theme_bw()
lsmeans.RVLTImm8 <- lsmeans(modelRVLT_imm_8, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.RVLTImm8,list(c1st,c2nd,c3rd),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.2336261 0.08984582 Inf
## structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) 0.0866195 0.09099025 Inf
## structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) 0.3202457 0.09032617 Inf
## z.ratio p.value
## 2.600 0.0093
## 0.952 0.3411
## 3.545 0.0004
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
modelRVLT_del_8<- lmer(RVLT_Delayed_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= Tracking.data_long)
summary(modelRVLT_del_8)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 130446.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8037 -0.5658 -0.0375 0.5152 5.0752
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.527 2.351
## Residual 7.723 2.779
## Number of obs: 24749, groups: ID, 8467
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 8.007e+00 4.324e-01
## timefactor2 5.565e-01 6.415e-02
## timefactor3 1.144e+00 6.477e-02
## PandemicFU2 data collected before COVID-19 4.631e-01 8.071e-02
## Age 2.747e-02 4.101e-03
## SexM -7.259e-01 7.002e-02
## EducationHigh School Diploma 4.000e-01 9.856e-02
## EducationLess than High School Diploma 6.261e-01 1.394e-01
## EducationSome College 4.204e-01 1.226e-01
## EthnicityWhite 8.938e-01 1.960e-01
## IncomeLevel>$150k 7.253e-01 1.830e-01
## IncomeLevel$100-150k 6.365e-01 1.463e-01
## IncomeLevel$20-50k 1.841e-01 9.742e-02
## IncomeLevel$50-100k 5.737e-01 1.049e-01
## BMI -1.728e-02 6.615e-03
## CESD.20.1 -3.505e-02 7.875e-03
## SmokingStatusFormer Smoker -3.424e-02 1.343e-01
## SmokingStatusNever Smoked 2.258e-01 1.397e-01
## SmokingStatusOccasional Smoker 1.609e-01 2.647e-01
## RelationshipstatusMarried -1.034e-02 1.145e-01
## RelationshipstatusSeparated -4.531e-02 2.254e-01
## RelationshipstatusSingle -1.684e-01 1.546e-01
## RelationshipstatusWidowed -1.316e-01 1.563e-01
## LivingstatusAssisted Living -1.121e+00 4.643e-01
## LivingstatusHouse 7.413e-02 1.032e-01
## LivingstatusOther -7.573e-02 3.852e-01
## AnxietyYes 1.417e-02 1.376e-01
## MoodDisordYes -1.363e-01 9.978e-02
## Chronicconditions -4.778e-02 1.613e-02
## PASE_TOTALbaseline 3.187e-03 4.696e-04
## timefactor2:PandemicFU2 data collected before COVID-19 -6.479e-02 8.674e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -3.469e-01 8.719e-02
## df t value
## (Intercept) 8.543e+03 18.516
## timefactor2 1.638e+04 8.674
## timefactor3 1.647e+04 17.655
## PandemicFU2 data collected before COVID-19 1.803e+04 5.738
## Age 8.419e+03 6.698
## SexM 8.404e+03 -10.367
## EducationHigh School Diploma 8.411e+03 4.058
## EducationLess than High School Diploma 8.511e+03 4.491
## EducationSome College 8.367e+03 3.430
## EthnicityWhite 8.447e+03 4.561
## IncomeLevel>$150k 8.400e+03 3.963
## IncomeLevel$100-150k 8.384e+03 4.350
## IncomeLevel$20-50k 8.419e+03 1.890
## IncomeLevel$50-100k 8.409e+03 5.467
## BMI 8.390e+03 -2.612
## CESD.20.1 8.419e+03 -4.450
## SmokingStatusFormer Smoker 8.400e+03 -0.255
## SmokingStatusNever Smoked 8.400e+03 1.616
## SmokingStatusOccasional Smoker 8.376e+03 0.608
## RelationshipstatusMarried 8.418e+03 -0.090
## RelationshipstatusSeparated 8.459e+03 -0.201
## RelationshipstatusSingle 8.402e+03 -1.089
## RelationshipstatusWidowed 8.422e+03 -0.842
## LivingstatusAssisted Living 8.451e+03 -2.414
## LivingstatusHouse 8.424e+03 0.719
## LivingstatusOther 8.385e+03 -0.197
## AnxietyYes 8.401e+03 0.103
## MoodDisordYes 8.398e+03 -1.366
## Chronicconditions 8.402e+03 -2.962
## PASE_TOTALbaseline 8.405e+03 6.786
## timefactor2:PandemicFU2 data collected before COVID-19 1.638e+04 -0.747
## timefactor3:PandemicFU2 data collected before COVID-19 1.642e+04 -3.979
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## timefactor3 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 9.71e-09 ***
## Age 2.24e-11 ***
## SexM < 2e-16 ***
## EducationHigh School Diploma 4.99e-05 ***
## EducationLess than High School Diploma 7.19e-06 ***
## EducationSome College 0.000607 ***
## EthnicityWhite 5.17e-06 ***
## IncomeLevel>$150k 7.46e-05 ***
## IncomeLevel$100-150k 1.38e-05 ***
## IncomeLevel$20-50k 0.058765 .
## IncomeLevel$50-100k 4.70e-08 ***
## BMI 0.009021 **
## CESD.20.1 8.68e-06 ***
## SmokingStatusFormer Smoker 0.798693
## SmokingStatusNever Smoked 0.106064
## SmokingStatusOccasional Smoker 0.543271
## RelationshipstatusMarried 0.928035
## RelationshipstatusSeparated 0.840732
## RelationshipstatusSingle 0.276040
## RelationshipstatusWidowed 0.399758
## LivingstatusAssisted Living 0.015787 *
## LivingstatusHouse 0.472411
## LivingstatusOther 0.844147
## AnxietyYes 0.917977
## MoodDisordYes 0.171981
## Chronicconditions 0.003066 **
## PASE_TOTALbaseline 1.23e-11 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.455094
## timefactor3:PandemicFU2 data collected before COVID-19 6.96e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_del_8, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.00794 0.2122547 Inf 9.591932 10.42396
## FU2 data collected before COVID-19 10.47108 0.2122193 Inf 10.055139 10.88702
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.56442 0.2124816 Inf 10.147960 10.98087
## FU2 data collected before COVID-19 10.96276 0.2124253 Inf 10.546414 11.37911
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.15146 0.2126820 Inf 10.734615 11.56831
## FU2 data collected before COVID-19 11.26772 0.2123699 Inf 10.851481 11.68396
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_8, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4631368 0.08070721 Inf -5.738 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3983447 0.08169290 Inf -4.876 <.0001
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1162545 0.08219608 Inf -1.414 0.1573
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_del_8, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4631368 0.08070721 Inf -0.6213201 -0.30495362
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3983447 0.08169290 Inf -0.5584598 -0.23822954
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1162545 0.08219608 Inf -0.2773558 0.04484687
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTdelayed_lsmeans_8 <- summary(lsmeans(modelRVLT_del_8, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTdelayed_lsmeans_8$Time<-NA
RVLTdelayed_lsmeans_8$Time[RVLTdelayed_lsmeans_8$timefactor==1]<-"Baseline"
RVLTdelayed_lsmeans_8$Time[RVLTdelayed_lsmeans_8$timefactor==2]<-"Follow-up 1"
RVLTdelayed_lsmeans_8$Time[RVLTdelayed_lsmeans_8$timefactor==3]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_8, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "RVLT Delayed Score", title = "RVLT Delayed Score from Baseline to FU2 by Pandemic status") +
theme_bw()
lsmeans.RVLTDel8 <- lsmeans(modelRVLT_del_8, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.RVLTDel8,list(c1st,c2nd,c3rd),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.0647922 0.08674026 Inf
## structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) 0.2820902 0.08807138 Inf
## structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) 0.3468824 0.08718526 Inf
## z.ratio p.value
## 0.747 0.4551
## 3.203 0.0014
## 3.979 0.0001
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
modelMAT_8<- lmer(MAT_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= Tracking.data_long)
summary(modelMAT_8)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 124671.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0904 -0.4700 -0.0150 0.4268 4.9654
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.976 2.231
## Residual 7.551 2.748
## Number of obs: 23820, groups: ID, 8467
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 9.839e+00 4.199e-01
## timefactor2 1.089e+00 6.490e-02
## timefactor3 -2.735e-01 6.533e-02
## PandemicFU2 data collected before COVID-19 5.771e-02 7.847e-02
## Age -1.312e-03 3.987e-03
## SexM -1.035e+00 6.793e-02
## EducationHigh School Diploma 5.987e-03 9.568e-02
## EducationLess than High School Diploma 3.372e-02 1.359e-01
## EducationSome College 5.522e-02 1.187e-01
## EthnicityWhite 1.339e+00 1.905e-01
## IncomeLevel>$150k 9.128e-01 1.776e-01
## IncomeLevel$100-150k 9.812e-01 1.419e-01
## IncomeLevel$20-50k 3.672e-01 9.463e-02
## IncomeLevel$50-100k 7.386e-01 1.019e-01
## BMI -2.572e-02 6.409e-03
## CESD.20.1 -4.432e-02 7.635e-03
## SmokingStatusFormer Smoker 1.818e-01 1.301e-01
## SmokingStatusNever Smoked 1.273e-01 1.354e-01
## SmokingStatusOccasional Smoker 1.427e-01 2.567e-01
## RelationshipstatusMarried 1.923e-01 1.112e-01
## RelationshipstatusSeparated -1.579e-01 2.184e-01
## RelationshipstatusSingle 2.988e-01 1.500e-01
## RelationshipstatusWidowed -1.158e-01 1.520e-01
## LivingstatusAssisted Living -7.079e-01 4.507e-01
## LivingstatusHouse -1.705e-01 1.002e-01
## LivingstatusOther -3.714e-01 3.746e-01
## AnxietyYes 9.797e-02 1.333e-01
## MoodDisordYes 2.718e-01 9.668e-02
## Chronicconditions -6.300e-02 1.568e-02
## PASE_TOTALbaseline -9.834e-04 4.551e-04
## timefactor2:PandemicFU2 data collected before COVID-19 -4.400e-01 8.757e-02
## timefactor3:PandemicFU2 data collected before COVID-19 6.328e-02 8.786e-02
## df t value
## (Intercept) 8.502e+03 23.435
## timefactor2 1.569e+04 16.774
## timefactor3 1.575e+04 -4.187
## PandemicFU2 data collected before COVID-19 1.795e+04 0.735
## Age 8.412e+03 -0.329
## SexM 8.346e+03 -15.230
## EducationHigh School Diploma 8.363e+03 0.063
## EducationLess than High School Diploma 8.559e+03 0.248
## EducationSome College 8.270e+03 0.465
## EthnicityWhite 8.436e+03 7.030
## IncomeLevel>$150k 8.342e+03 5.140
## IncomeLevel$100-150k 8.312e+03 6.917
## IncomeLevel$20-50k 8.392e+03 3.880
## IncomeLevel$50-100k 8.371e+03 7.248
## BMI 8.290e+03 -4.014
## CESD.20.1 8.333e+03 -5.805
## SmokingStatusFormer Smoker 8.311e+03 1.397
## SmokingStatusNever Smoked 8.311e+03 0.940
## SmokingStatusOccasional Smoker 8.291e+03 0.556
## RelationshipstatusMarried 8.379e+03 1.730
## RelationshipstatusSeparated 8.369e+03 -0.723
## RelationshipstatusSingle 8.356e+03 1.991
## RelationshipstatusWidowed 8.421e+03 -0.762
## LivingstatusAssisted Living 8.384e+03 -1.571
## LivingstatusHouse 8.399e+03 -1.701
## LivingstatusOther 8.387e+03 -0.991
## AnxietyYes 8.313e+03 0.735
## MoodDisordYes 8.305e+03 2.811
## Chronicconditions 8.386e+03 -4.019
## PASE_TOTALbaseline 8.315e+03 -2.161
## timefactor2:PandemicFU2 data collected before COVID-19 1.566e+04 -5.024
## timefactor3:PandemicFU2 data collected before COVID-19 1.569e+04 0.720
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## timefactor3 2.85e-05 ***
## PandemicFU2 data collected before COVID-19 0.462063
## Age 0.742155
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.950105
## EducationLess than High School Diploma 0.804084
## EducationSome College 0.641873
## EthnicityWhite 2.22e-12 ***
## IncomeLevel>$150k 2.81e-07 ***
## IncomeLevel$100-150k 4.95e-12 ***
## IncomeLevel$20-50k 0.000105 ***
## IncomeLevel$50-100k 4.60e-13 ***
## BMI 6.03e-05 ***
## CESD.20.1 6.67e-09 ***
## SmokingStatusFormer Smoker 0.162483
## SmokingStatusNever Smoked 0.347271
## SmokingStatusOccasional Smoker 0.578360
## RelationshipstatusMarried 0.083696 .
## RelationshipstatusSeparated 0.469871
## RelationshipstatusSingle 0.046462 *
## RelationshipstatusWidowed 0.446282
## LivingstatusAssisted Living 0.116282
## LivingstatusHouse 0.088956 .
## LivingstatusOther 0.321493
## AnxietyYes 0.462461
## MoodDisordYes 0.004944 **
## Chronicconditions 5.91e-05 ***
## PASE_TOTALbaseline 0.030741 *
## timefactor2:PandemicFU2 data collected before COVID-19 5.11e-07 ***
## timefactor3:PandemicFU2 data collected before COVID-19 0.471359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelMAT_8, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.313348 0.2061680 Inf 8.909266 9.717430
## FU2 data collected before COVID-19 9.371061 0.2061532 Inf 8.967008 9.775114
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.402054 0.2069456 Inf 9.996448 10.807660
## FU2 data collected before COVID-19 10.019796 0.2066847 Inf 9.614701 10.424891
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.039853 0.2068304 Inf 8.634472 9.445233
## FU2 data collected before COVID-19 9.160847 0.2065923 Inf 8.755933 9.565760
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_8, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0577128 0.07847061 Inf -0.735 0.4621
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.3822583 0.08144610 Inf 4.693 <.0001
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1209942 0.08174658 Inf -1.480 0.1388
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelMAT_8, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0577128 0.07847061 Inf -0.2115123 0.0960868
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.3822583 0.08144610 Inf 0.2226269 0.5418897
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1209942 0.08174658 Inf -0.2812146 0.0392261
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
MAT_lsmeans_8 <- summary(lsmeans(modelMAT_8, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
MAT_lsmeans_8$Time<-NA
MAT_lsmeans_8$Time[MAT_lsmeans_8$timefactor==1]<-"Baseline"
MAT_lsmeans_8$Time[MAT_lsmeans_8$timefactor==2]<-"Follow-up 1"
MAT_lsmeans_8$Time[MAT_lsmeans_8$timefactor==3]<-"Follow-up 2"
ggplot(MAT_lsmeans_8, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "MAT Score", title = "Mental Alteration Test Score from Baseline to FU2 by Pandemic status") +
theme_bw()
lsmeans.MAT8 <- lsmeans(modelMAT_8, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.MAT8,list(c1st,c2nd,c3rd),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 0.4399710 0.08757185 Inf
## structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -0.5032525 0.09036545 Inf
## structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -0.0632814 0.08785589 Inf
## z.ratio p.value
## 5.024 <.0001
## -5.569 <.0001
## -0.720 0.4713
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
modelAnimals_8<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= Tracking.data_long)
summary(modelAnimals_8)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 116933.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3609 -0.5630 -0.0152 0.5500 4.7486
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.913 2.217
## Residual 3.633 1.906
## Number of obs: 25010, groups: ID, 8467
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 9.074e+00 3.740e-01
## timefactor2 -1.428e-01 4.377e-02
## timefactor3 -5.163e-02 4.426e-02
## PandemicFU2 data collected before COVID-19 1.729e-01 6.498e-02
## Age -1.770e-03 3.552e-03
## SexM -3.122e-01 6.067e-02
## EducationHigh School Diploma 2.966e-01 8.542e-02
## EducationLess than High School Diploma 4.156e-01 1.205e-01
## EducationSome College 2.496e-01 1.063e-01
## EthnicityWhite 1.308e+00 1.694e-01
## IncomeLevel>$150k 6.304e-01 1.586e-01
## IncomeLevel$100-150k 5.297e-01 1.268e-01
## IncomeLevel$20-50k 8.052e-02 8.438e-02
## IncomeLevel$50-100k 3.436e-01 9.092e-02
## BMI -1.565e-02 5.733e-03
## CESD.20.1 -3.527e-02 6.822e-03
## SmokingStatusFormer Smoker 2.008e-01 1.164e-01
## SmokingStatusNever Smoked 2.082e-01 1.211e-01
## SmokingStatusOccasional Smoker 2.770e-01 2.295e-01
## RelationshipstatusMarried -1.813e-01 9.922e-02
## RelationshipstatusSeparated -1.565e-01 1.952e-01
## RelationshipstatusSingle -7.962e-02 1.340e-01
## RelationshipstatusWidowed -2.110e-01 1.354e-01
## LivingstatusAssisted Living -1.183e-01 4.018e-01
## LivingstatusHouse 3.409e-01 8.937e-02
## LivingstatusOther 1.257e-01 3.334e-01
## AnxietyYes -7.203e-02 1.192e-01
## MoodDisordYes 2.103e-01 8.649e-02
## Chronicconditions -1.593e-02 1.398e-02
## PASE_TOTALbaseline 9.462e-04 4.068e-04
## timefactor2:PandemicFU2 data collected before COVID-19 5.329e-02 5.918e-02
## timefactor3:PandemicFU2 data collected before COVID-19 7.531e-02 5.954e-02
## df t value
## (Intercept) 8.512e+03 24.266
## timefactor2 1.658e+04 -3.262
## timefactor3 1.665e+04 -1.166
## PandemicFU2 data collected before COVID-19 1.482e+04 2.660
## Age 8.432e+03 -0.498
## SexM 8.436e+03 -5.145
## EducationHigh School Diploma 8.447e+03 3.473
## EducationLess than High School Diploma 8.470e+03 3.449
## EducationSome College 8.421e+03 2.348
## EthnicityWhite 8.417e+03 7.722
## IncomeLevel>$150k 8.436e+03 3.974
## IncomeLevel$100-150k 8.427e+03 4.177
## IncomeLevel$20-50k 8.439e+03 0.954
## IncomeLevel$50-100k 8.439e+03 3.779
## BMI 8.428e+03 -2.729
## CESD.20.1 8.441e+03 -5.170
## SmokingStatusFormer Smoker 8.438e+03 1.725
## SmokingStatusNever Smoked 8.436e+03 1.720
## SmokingStatusOccasional Smoker 8.426e+03 1.207
## RelationshipstatusMarried 8.454e+03 -1.827
## RelationshipstatusSeparated 8.461e+03 -0.802
## RelationshipstatusSingle 8.446e+03 -0.594
## RelationshipstatusWidowed 8.440e+03 -1.559
## LivingstatusAssisted Living 8.441e+03 -0.295
## LivingstatusHouse 8.450e+03 3.815
## LivingstatusOther 8.376e+03 0.377
## AnxietyYes 8.437e+03 -0.604
## MoodDisordYes 8.440e+03 2.431
## Chronicconditions 8.426e+03 -1.140
## PASE_TOTALbaseline 8.430e+03 2.326
## timefactor2:PandemicFU2 data collected before COVID-19 1.658e+04 0.900
## timefactor3:PandemicFU2 data collected before COVID-19 1.662e+04 1.265
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 0.001108 **
## timefactor3 0.243445
## PandemicFU2 data collected before COVID-19 0.007818 **
## Age 0.618234
## SexM 2.74e-07 ***
## EducationHigh School Diploma 0.000518 ***
## EducationLess than High School Diploma 0.000566 ***
## EducationSome College 0.018897 *
## EthnicityWhite 1.27e-14 ***
## IncomeLevel>$150k 7.12e-05 ***
## IncomeLevel$100-150k 2.99e-05 ***
## IncomeLevel$20-50k 0.339992
## IncomeLevel$50-100k 0.000159 ***
## BMI 0.006359 **
## CESD.20.1 2.39e-07 ***
## SmokingStatusFormer Smoker 0.084541 .
## SmokingStatusNever Smoked 0.085502 .
## SmokingStatusOccasional Smoker 0.227570
## RelationshipstatusMarried 0.067687 .
## RelationshipstatusSeparated 0.422670
## RelationshipstatusSingle 0.552441
## RelationshipstatusWidowed 0.119095
## LivingstatusAssisted Living 0.768355
## LivingstatusHouse 0.000137 ***
## LivingstatusOther 0.706144
## AnxietyYes 0.545818
## MoodDisordYes 0.015064 *
## Chronicconditions 0.254359
## PASE_TOTALbaseline 0.020055 *
## timefactor2:PandemicFU2 data collected before COVID-19 0.367881
## timefactor3:PandemicFU2 data collected before COVID-19 0.205960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelAnimals_8, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.745959 0.1828005 Inf 9.387677 10.104242
## FU2 data collected before COVID-19 9.918814 0.1829353 Inf 9.560268 10.277361
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.603186 0.1828656 Inf 9.244775 9.961596
## FU2 data collected before COVID-19 9.829329 0.1829668 Inf 9.470720 10.187937
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.694331 0.1829621 Inf 9.335732 10.052930
## FU2 data collected before COVID-19 9.942493 0.1829846 Inf 9.583850 10.301137
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_8, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1728549 0.06497816 Inf -2.660 0.0078
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2261432 0.06527312 Inf -3.465 0.0005
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2481626 0.06562061 Inf -3.782 0.0002
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelAnimals_8, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1728549 0.06497816 Inf -0.3002097 -0.04550002
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2261432 0.06527312 Inf -0.3540762 -0.09821027
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2481626 0.06562061 Inf -0.3767766 -0.11954856
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
Animals_lsmeans_8 <- summary(lsmeans(modelAnimals_8, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
Animals_lsmeans_8$Time<-NA
Animals_lsmeans_8$Time[Animals_lsmeans_8$timefactor==1]<-"Baseline"
Animals_lsmeans_8$Time[Animals_lsmeans_8$timefactor==2]<-"Follow-up 1"
Animals_lsmeans_8$Time[Animals_lsmeans_8$timefactor==3]<-"Follow-up 2"
ggplot(Animals_lsmeans_8, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "Animal Fluency (words)", title = "Animal Fluency Score from Baseline to FU2 by Pandemic status") +
theme_bw()
lsmeans.Animals8 <- lsmeans(modelAnimals_8, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.Animals8,list(c1st,c2nd,c3rd),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) -0.05328836 0.05917816 Inf
## structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -0.02201937 0.05985472 Inf
## structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -0.07530773 0.05954112 Inf
## z.ratio p.value
## -0.900 0.3679
## -0.368 0.7130
## -1.265 0.2059
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
All models use normalized cognitive scores. Each model is adjusted for baseline age, sex, education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, baseline PASE score, and baseline cognitive performance
modelRVLT_imm_adj10<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelRVLT_imm_adj10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
## (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 85610
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7099 -0.5660 -0.0356 0.5277 3.8353
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.089 2.022
## Residual 7.350 2.711
## Number of obs: 16420, groups: ID, 8448
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 7.454e+00 4.279e-01
## timefactor2 3.108e-01 6.352e-02
## PandemicFU2 data collected before COVID-19 1.794e-01 7.598e-02
## Age -1.474e-02 4.052e-03
## SexM -5.715e-01 6.915e-02
## EducationHigh School Diploma 2.724e-01 9.700e-02
## EducationLess than High School Diploma 5.141e-01 1.374e-01
## EducationSome College 2.371e-01 1.205e-01
## EthnicityWhite 4.852e-01 1.922e-01
## IncomeLevel>$150k 4.738e-01 1.804e-01
## IncomeLevel$100-150k 4.078e-01 1.439e-01
## IncomeLevel$20-50k 1.463e-01 9.587e-02
## IncomeLevel$50-100k 4.242e-01 1.033e-01
## BMI -1.687e-02 6.507e-03
## CESD.10baseline -2.594e-02 7.747e-03
## SmokingStatusFormer Smoker 1.758e-01 1.321e-01
## SmokingStatusNever Smoked 2.787e-01 1.375e-01
## SmokingStatusOccasional Smoker -2.838e-02 2.599e-01
## RelationshipstatusMarried 3.161e-01 1.127e-01
## RelationshipstatusSeparated 2.365e-01 2.221e-01
## RelationshipstatusSingle 1.878e-01 1.522e-01
## RelationshipstatusWidowed 1.763e-02 1.539e-01
## LivingstatusAssisted Living -8.328e-01 4.570e-01
## LivingstatusHouse 4.362e-02 1.015e-01
## LivingstatusOther -3.584e-01 3.776e-01
## AnxietyYes 6.747e-02 1.354e-01
## MoodDisordYes -1.642e-01 9.819e-02
## Chronicconditions -2.306e-02 1.587e-02
## PASE_TOTALbaseline 2.277e-03 4.624e-04
## RVLT_Immediate_Normedbaseline 3.512e-01 8.160e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -8.311e-02 8.553e-02
## df t value
## (Intercept) 8.446e+03 17.419
## timefactor2 8.281e+03 4.893
## PandemicFU2 data collected before COVID-19 1.448e+04 2.361
## Age 8.385e+03 -3.638
## SexM 8.376e+03 -8.264
## EducationHigh School Diploma 8.375e+03 2.808
## EducationLess than High School Diploma 8.502e+03 3.741
## EducationSome College 8.296e+03 1.967
## EthnicityWhite 8.291e+03 2.524
## IncomeLevel>$150k 8.371e+03 2.627
## IncomeLevel$100-150k 8.322e+03 2.834
## IncomeLevel$20-50k 8.379e+03 1.526
## IncomeLevel$50-100k 8.375e+03 4.105
## BMI 8.345e+03 -2.593
## CESD.10baseline 8.375e+03 -3.348
## SmokingStatusFormer Smoker 8.393e+03 1.330
## SmokingStatusNever Smoked 8.392e+03 2.027
## SmokingStatusOccasional Smoker 8.265e+03 -0.109
## RelationshipstatusMarried 8.393e+03 2.804
## RelationshipstatusSeparated 8.461e+03 1.065
## RelationshipstatusSingle 8.382e+03 1.234
## RelationshipstatusWidowed 8.398e+03 0.115
## LivingstatusAssisted Living 8.435e+03 -1.823
## LivingstatusHouse 8.374e+03 0.430
## LivingstatusOther 8.287e+03 -0.949
## AnxietyYes 8.368e+03 0.498
## MoodDisordYes 8.367e+03 -1.672
## Chronicconditions 8.362e+03 -1.453
## PASE_TOTALbaseline 8.367e+03 4.924
## RVLT_Immediate_Normedbaseline 8.388e+03 43.034
## timefactor2:PandemicFU2 data collected before COVID-19 8.230e+03 -0.972
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 1.01e-06 ***
## PandemicFU2 data collected before COVID-19 0.018254 *
## Age 0.000276 ***
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.004991 **
## EducationLess than High School Diploma 0.000184 ***
## EducationSome College 0.049221 *
## EthnicityWhite 0.011623 *
## IncomeLevel>$150k 0.008637 **
## IncomeLevel$100-150k 0.004601 **
## IncomeLevel$20-50k 0.127043
## IncomeLevel$50-100k 4.08e-05 ***
## BMI 0.009525 **
## CESD.10baseline 0.000817 ***
## SmokingStatusFormer Smoker 0.183503
## SmokingStatusNever Smoked 0.042735 *
## SmokingStatusOccasional Smoker 0.913045
## RelationshipstatusMarried 0.005054 **
## RelationshipstatusSeparated 0.286915
## RelationshipstatusSingle 0.217165
## RelationshipstatusWidowed 0.908794
## LivingstatusAssisted Living 0.068413 .
## LivingstatusHouse 0.667408
## LivingstatusOther 0.342513
## AnxietyYes 0.618158
## MoodDisordYes 0.094596 .
## Chronicconditions 0.146264
## PASE_TOTALbaseline 8.63e-07 ***
## RVLT_Immediate_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.331232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.40541 0.2077597 Inf 9.998208 10.81261
## FU2 data collected before COVID-19 10.58478 0.2078880 Inf 10.177330 10.99224
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.71623 0.2079816 Inf 10.308597 11.12387
## FU2 data collected before COVID-19 10.81250 0.2077872 Inf 10.405241 11.21975
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.17937348 0.07598370 Inf -2.361 0.0182
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.09626301 0.07655159 Inf -1.257 0.2086
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.17937348 0.07598370 Inf -0.3282988 -0.03044816
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.09626301 0.07655159 Inf -0.2463014 0.05377536
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTimmediate_lsmeans_adj10 <- summary(lsmeans(modelRVLT_imm_adj10, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTimmediate_lsmeans_adj10$Time<-NA
RVLTimmediate_lsmeans_adj10$Time[RVLTimmediate_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj10$Time[RVLTimmediate_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
lsmeans.RVLTImm10 <- lsmeans(modelRVLT_imm_adj10, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.RVLTImm10,list(c4th),by=NULL)
## contrast estimate SE df z.ratio
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.08311047 0.08553164 Inf 0.972
## p.value
## 0.3312
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
modelRVLT_del_adj10<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelRVLT_del_adj10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
## (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 84249.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8988 -0.5550 -0.0364 0.5114 4.0165
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.115 2.029
## Residual 6.942 2.635
## Number of obs: 16282, groups: ID, 8435
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 6.568e+00 4.255e-01
## timefactor2 5.852e-01 6.217e-02
## PandemicFU2 data collected before COVID-19 2.664e-01 7.498e-02
## Age -6.290e-03 4.024e-03
## SexM -5.303e-01 6.846e-02
## EducationHigh School Diploma 2.956e-01 9.617e-02
## EducationLess than High School Diploma 3.886e-01 1.367e-01
## EducationSome College 3.012e-01 1.194e-01
## EthnicityWhite 7.048e-01 1.916e-01
## IncomeLevel>$150k 4.192e-01 1.785e-01
## IncomeLevel$100-150k 3.324e-01 1.426e-01
## IncomeLevel$20-50k 1.598e-01 9.505e-02
## IncomeLevel$50-100k 4.515e-01 1.024e-01
## BMI -1.834e-02 6.446e-03
## CESD.10baseline -2.162e-02 7.687e-03
## SmokingStatusFormer Smoker 9.409e-02 1.308e-01
## SmokingStatusNever Smoked 2.694e-01 1.361e-01
## SmokingStatusOccasional Smoker 6.592e-02 2.578e-01
## RelationshipstatusMarried 8.316e-02 1.118e-01
## RelationshipstatusSeparated -5.816e-02 2.203e-01
## RelationshipstatusSingle 2.294e-02 1.508e-01
## RelationshipstatusWidowed -1.445e-01 1.526e-01
## LivingstatusAssisted Living -1.047e+00 4.534e-01
## LivingstatusHouse 1.008e-01 1.007e-01
## LivingstatusOther -2.094e-01 3.751e-01
## AnxietyYes 1.046e-01 1.341e-01
## MoodDisordYes -1.729e-01 9.725e-02
## Chronicconditions -3.090e-02 1.573e-02
## PASE_TOTALbaseline 2.308e-03 4.584e-04
## RVLT_Delayed_Normedbaseline 3.921e-01 8.304e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -2.805e-01 8.362e-02
## df t value
## (Intercept) 8.471e+03 15.437
## timefactor2 8.230e+03 9.413
## PandemicFU2 data collected before COVID-19 1.430e+04 3.553
## Age 8.386e+03 -1.563
## SexM 8.357e+03 -7.746
## EducationHigh School Diploma 8.359e+03 3.074
## EducationLess than High School Diploma 8.552e+03 2.843
## EducationSome College 8.276e+03 2.523
## EthnicityWhite 8.393e+03 3.678
## IncomeLevel>$150k 8.347e+03 2.348
## IncomeLevel$100-150k 8.319e+03 2.331
## IncomeLevel$20-50k 8.379e+03 1.681
## IncomeLevel$50-100k 8.359e+03 4.409
## BMI 8.319e+03 -2.844
## CESD.10baseline 8.388e+03 -2.813
## SmokingStatusFormer Smoker 8.369e+03 0.719
## SmokingStatusNever Smoked 8.366e+03 1.979
## SmokingStatusOccasional Smoker 8.288e+03 0.256
## RelationshipstatusMarried 8.349e+03 0.744
## RelationshipstatusSeparated 8.470e+03 -0.264
## RelationshipstatusSingle 8.335e+03 0.152
## RelationshipstatusWidowed 8.369e+03 -0.947
## LivingstatusAssisted Living 8.465e+03 -2.308
## LivingstatusHouse 8.376e+03 1.001
## LivingstatusOther 8.341e+03 -0.558
## AnxietyYes 8.338e+03 0.780
## MoodDisordYes 8.344e+03 -1.778
## Chronicconditions 8.348e+03 -1.964
## PASE_TOTALbaseline 8.358e+03 5.034
## RVLT_Delayed_Normedbaseline 8.363e+03 47.224
## timefactor2:PandemicFU2 data collected before COVID-19 8.170e+03 -3.355
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 0.000382 ***
## Age 0.118036
## SexM 1.06e-14 ***
## EducationHigh School Diploma 0.002121 **
## EducationLess than High School Diploma 0.004482 **
## EducationSome College 0.011644 *
## EthnicityWhite 0.000237 ***
## IncomeLevel>$150k 0.018884 *
## IncomeLevel$100-150k 0.019797 *
## IncomeLevel$20-50k 0.092806 .
## IncomeLevel$50-100k 1.05e-05 ***
## BMI 0.004460 **
## CESD.10baseline 0.004917 **
## SmokingStatusFormer Smoker 0.472075
## SmokingStatusNever Smoked 0.047843 *
## SmokingStatusOccasional Smoker 0.798193
## RelationshipstatusMarried 0.456884
## RelationshipstatusSeparated 0.791753
## RelationshipstatusSingle 0.879086
## RelationshipstatusWidowed 0.343720
## LivingstatusAssisted Living 0.020999 *
## LivingstatusHouse 0.316968
## LivingstatusOther 0.576638
## AnxietyYes 0.435501
## MoodDisordYes 0.075487 .
## Chronicconditions 0.049550 *
## PASE_TOTALbaseline 4.89e-07 ***
## RVLT_Delayed_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.000798 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.48443 0.2062365 Inf 10.08022 10.88865
## FU2 data collected before COVID-19 10.75083 0.2063549 Inf 10.34639 11.15528
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.06962 0.2064417 Inf 10.66500 11.47423
## FU2 data collected before COVID-19 11.05548 0.2062749 Inf 10.65119 11.45978
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.26640234 0.07498067 Inf -3.553 0.0004
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.01413065 0.07552241 Inf 0.187 0.8516
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.26640234 0.07498067 Inf -0.4133618 -0.1194429
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.01413065 0.07552241 Inf -0.1338905 0.1621518
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTdelayed_lsmeans_adj10 <- summary(lsmeans(modelRVLT_del_adj10, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTdelayed_lsmeans_adj10$Time<-NA
RVLTdelayed_lsmeans_adj10$Time[RVLTdelayed_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj10$Time[RVLTdelayed_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
lsmeans.RVLTDel10 <- lsmeans(modelRVLT_del_adj10, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.RVLTDel10,list(c4th),by=NULL)
## contrast estimate SE df z.ratio
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) 0.280533 0.08362461 Inf 3.355
## p.value
## 0.0008
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
modelMAT_adj10<- lmer(MAT_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelMAT_adj10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline + (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 79636.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4748 -0.5205 -0.0783 0.3697 4.9556
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.353 1.534
## Residual 8.304 2.882
## Number of obs: 15353, groups: ID, 8344
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 7.636e+00 4.088e-01
## timefactor2 -1.365e+00 7.034e-02
## PandemicFU2 data collected before COVID-19 -4.697e-01 7.582e-02
## Age -1.255e-02 3.827e-03
## SexM -1.093e+00 6.501e-02
## EducationHigh School Diploma 3.828e-03 9.152e-02
## EducationLess than High School Diploma -5.448e-02 1.317e-01
## EducationSome College -8.197e-02 1.129e-01
## EthnicityWhite 8.887e-01 1.833e-01
## IncomeLevel>$150k 2.230e-01 1.699e-01
## IncomeLevel$100-150k 2.581e-01 1.356e-01
## IncomeLevel$20-50k 1.562e-01 9.074e-02
## IncomeLevel$50-100k 1.873e-01 9.781e-02
## BMI -1.367e-02 6.105e-03
## CESD.10baseline -1.840e-02 7.302e-03
## SmokingStatusFormer Smoker 9.113e-02 1.241e-01
## SmokingStatusNever Smoked 1.816e-02 1.291e-01
## SmokingStatusOccasional Smoker 1.809e-01 2.445e-01
## RelationshipstatusMarried 2.547e-02 1.064e-01
## RelationshipstatusSeparated -5.661e-02 2.089e-01
## RelationshipstatusSingle 5.263e-01 1.434e-01
## RelationshipstatusWidowed -8.460e-02 1.459e-01
## LivingstatusAssisted Living -3.059e-01 4.319e-01
## LivingstatusHouse -2.065e-01 9.608e-02
## LivingstatusOther -3.766e-01 3.588e-01
## AnxietyYes 6.174e-02 1.271e-01
## MoodDisordYes 1.262e-01 9.213e-02
## Chronicconditions -3.402e-02 1.503e-02
## PASE_TOTALbaseline -6.223e-04 4.339e-04
## MAT_Normedbaseline 4.490e-01 8.549e-03
## timefactor2:PandemicFU2 data collected before COVID-19 5.097e-01 9.449e-02
## df t value
## (Intercept) 8.221e+03 18.676
## timefactor2 7.889e+03 -19.411
## PandemicFU2 data collected before COVID-19 1.472e+04 -6.195
## Age 8.180e+03 -3.278
## SexM 8.078e+03 -16.805
## EducationHigh School Diploma 8.077e+03 0.042
## EducationLess than High School Diploma 8.326e+03 -0.414
## EducationSome College 7.966e+03 -0.726
## EthnicityWhite 8.203e+03 4.849
## IncomeLevel>$150k 8.064e+03 1.313
## IncomeLevel$100-150k 8.039e+03 1.903
## IncomeLevel$20-50k 8.141e+03 1.722
## IncomeLevel$50-100k 8.095e+03 1.915
## BMI 7.968e+03 -2.239
## CESD.10baseline 8.039e+03 -2.520
## SmokingStatusFormer Smoker 8.022e+03 0.735
## SmokingStatusNever Smoked 8.023e+03 0.141
## SmokingStatusOccasional Smoker 7.892e+03 0.740
## RelationshipstatusMarried 8.133e+03 0.239
## RelationshipstatusSeparated 8.186e+03 -0.271
## RelationshipstatusSingle 8.103e+03 3.670
## RelationshipstatusWidowed 8.186e+03 -0.580
## LivingstatusAssisted Living 8.064e+03 -0.708
## LivingstatusHouse 8.146e+03 -2.150
## LivingstatusOther 8.143e+03 -1.049
## AnxietyYes 8.045e+03 0.486
## MoodDisordYes 8.017e+03 1.370
## Chronicconditions 8.136e+03 -2.264
## PASE_TOTALbaseline 8.031e+03 -1.434
## MAT_Normedbaseline 8.135e+03 52.521
## timefactor2:PandemicFU2 data collected before COVID-19 7.827e+03 5.394
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 5.97e-10 ***
## Age 0.001048 **
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.966640
## EducationLess than High School Diploma 0.679223
## EducationSome College 0.467668
## EthnicityWhite 1.26e-06 ***
## IncomeLevel>$150k 0.189326
## IncomeLevel$100-150k 0.057013 .
## IncomeLevel$20-50k 0.085189 .
## IncomeLevel$50-100k 0.055552 .
## BMI 0.025155 *
## CESD.10baseline 0.011742 *
## SmokingStatusFormer Smoker 0.462610
## SmokingStatusNever Smoked 0.888127
## SmokingStatusOccasional Smoker 0.459449
## RelationshipstatusMarried 0.810900
## RelationshipstatusSeparated 0.786349
## RelationshipstatusSingle 0.000244 ***
## RelationshipstatusWidowed 0.562045
## LivingstatusAssisted Living 0.478757
## LivingstatusHouse 0.031622 *
## LivingstatusOther 0.293997
## AnxietyYes 0.627040
## MoodDisordYes 0.170674
## Chronicconditions 0.023595 *
## PASE_TOTALbaseline 0.151566
## MAT_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 7.08e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelMAT_adj10, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.806895 0.1977972 Inf 10.419220 11.194571
## FU2 data collected before COVID-19 10.337165 0.1975870 Inf 9.949902 10.724429
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.441577 0.1975127 Inf 9.054459 9.828695
## FU2 data collected before COVID-19 9.481585 0.1974365 Inf 9.094617 9.868554
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_adj10, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.4697302 0.07582012 Inf 6.195 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0400084 0.07615539 Inf -0.525 0.5993
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelMAT_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.4697302 0.07582012 Inf 0.3211255 0.6183349
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0400084 0.07615539 Inf -0.1892702 0.1092535
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
MAT_lsmeans_adj10 <- summary(lsmeans(modelMAT_adj10, ~Pandemic|timefactor))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
MAT_lsmeans_adj10$Time<-NA
MAT_lsmeans_adj10$Time[MAT_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj10$Time[MAT_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
lsmeans.MAT10 <- lsmeans(modelMAT_adj10, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.MAT10,list(c4th),by=NULL)
## contrast estimate SE df z.ratio
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.5097385 0.09449385 Inf -5.394
## p.value
## <.0001
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
modelAnimals_adj10<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelAnimals_adj10)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
## (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 73835.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8973 -0.5565 -0.0188 0.5329 4.7771
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.379 1.543
## Residual 3.191 1.786
## Number of obs: 16543, groups: ID, 8454
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 5.080e+00 3.085e-01
## timefactor2 9.181e-02 4.176e-02
## PandemicFU2 data collected before COVID-19 1.678e-01 5.281e-02
## Age -1.415e-02 2.875e-03
## SexM -1.683e-01 4.917e-02
## EducationHigh School Diploma 2.332e-01 6.923e-02
## EducationLess than High School Diploma 1.463e-01 9.784e-02
## EducationSome College 1.784e-01 8.597e-02
## EthnicityWhite 4.594e-01 1.374e-01
## IncomeLevel>$150k 3.605e-03 1.286e-01
## IncomeLevel$100-150k 4.776e-02 1.028e-01
## IncomeLevel$20-50k -1.061e-01 6.835e-02
## IncomeLevel$50-100k 6.691e-02 7.369e-02
## BMI -9.107e-03 4.640e-03
## CESD.10baseline -1.015e-02 5.533e-03
## SmokingStatusFormer Smoker 1.414e-01 9.422e-02
## SmokingStatusNever Smoked 1.664e-01 9.802e-02
## SmokingStatusOccasional Smoker -2.532e-02 1.858e-01
## RelationshipstatusMarried 4.729e-02 8.046e-02
## RelationshipstatusSeparated 1.877e-01 1.583e-01
## RelationshipstatusSingle 5.719e-02 1.086e-01
## RelationshipstatusWidowed -1.872e-02 1.096e-01
## LivingstatusAssisted Living -2.391e-01 3.253e-01
## LivingstatusHouse 1.539e-01 7.248e-02
## LivingstatusOther -2.822e-01 2.687e-01
## AnxietyYes 1.026e-01 9.656e-02
## MoodDisordYes 7.169e-02 7.006e-02
## Chronicconditions -1.343e-02 1.131e-02
## PASE_TOTALbaseline 4.653e-04 3.293e-04
## Animal_Fluency_Normedbaseline 5.275e-01 7.168e-03
## timefactor2:PandemicFU2 data collected before COVID-19 2.120e-02 5.614e-02
## df t value
## (Intercept) 8.450e+03 16.465
## timefactor2 8.321e+03 2.199
## PandemicFU2 data collected before COVID-19 1.389e+04 3.178
## Age 8.378e+03 -4.920
## SexM 8.380e+03 -3.424
## EducationHigh School Diploma 8.392e+03 3.369
## EducationLess than High School Diploma 8.481e+03 1.496
## EducationSome College 8.339e+03 2.075
## EthnicityWhite 8.311e+03 3.343
## IncomeLevel>$150k 8.389e+03 0.028
## IncomeLevel$100-150k 8.350e+03 0.465
## IncomeLevel$20-50k 8.383e+03 -1.552
## IncomeLevel$50-100k 8.381e+03 0.908
## BMI 8.357e+03 -1.963
## CESD.10baseline 8.397e+03 -1.835
## SmokingStatusFormer Smoker 8.396e+03 1.501
## SmokingStatusNever Smoked 8.393e+03 1.698
## SmokingStatusOccasional Smoker 8.321e+03 -0.136
## RelationshipstatusMarried 8.417e+03 0.588
## RelationshipstatusSeparated 8.454e+03 1.186
## RelationshipstatusSingle 8.408e+03 0.527
## RelationshipstatusWidowed 8.387e+03 -0.171
## LivingstatusAssisted Living 8.405e+03 -0.735
## LivingstatusHouse 8.399e+03 2.123
## LivingstatusOther 8.242e+03 -1.050
## AnxietyYes 8.369e+03 1.063
## MoodDisordYes 8.389e+03 1.023
## Chronicconditions 8.356e+03 -1.188
## PASE_TOTALbaseline 8.370e+03 1.413
## Animal_Fluency_Normedbaseline 8.364e+03 73.588
## timefactor2:PandemicFU2 data collected before COVID-19 8.263e+03 0.378
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 0.027920 *
## PandemicFU2 data collected before COVID-19 0.001484 **
## Age 8.84e-07 ***
## SexM 0.000620 ***
## EducationHigh School Diploma 0.000758 ***
## EducationLess than High School Diploma 0.134805
## EducationSome College 0.038002 *
## EthnicityWhite 0.000833 ***
## IncomeLevel>$150k 0.977631
## IncomeLevel$100-150k 0.642151
## IncomeLevel$20-50k 0.120713
## IncomeLevel$50-100k 0.363888
## BMI 0.049706 *
## CESD.10baseline 0.066550 .
## SmokingStatusFormer Smoker 0.133337
## SmokingStatusNever Smoked 0.089526 .
## SmokingStatusOccasional Smoker 0.891580
## RelationshipstatusMarried 0.556703
## RelationshipstatusSeparated 0.235685
## RelationshipstatusSingle 0.598337
## RelationshipstatusWidowed 0.864439
## LivingstatusAssisted Living 0.462495
## LivingstatusHouse 0.033777 *
## LivingstatusOther 0.293663
## AnxietyYes 0.287883
## MoodDisordYes 0.306261
## Chronicconditions 0.235037
## PASE_TOTALbaseline 0.157657
## Animal_Fluency_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.705696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelAnimals_adj10, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.832780 0.1478945 Inf 9.542912 10.12265
## FU2 data collected before COVID-19 10.000617 0.1479464 Inf 9.710647 10.29059
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.924592 0.1479894 Inf 9.634538 10.21465
## FU2 data collected before COVID-19 10.113629 0.1479770 Inf 9.823600 10.40366
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_adj10, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1678372 0.05280608 Inf -3.178 0.0015
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1890379 0.05321063 Inf -3.553 0.0004
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelAnimals_adj10, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1678372 0.05280608 Inf -0.2713352 -0.06433915
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1890379 0.05321063 Inf -0.2933288 -0.08474695
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
Animals_lsmeans_adj10 <- summary(lsmeans(modelAnimals_adj10, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
Animals_lsmeans_adj10$Time<-NA
Animals_lsmeans_adj10$Time[Animals_lsmeans_adj10$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj10$Time[Animals_lsmeans_adj10$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj10, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
lsmeans.Animals10 <- lsmeans(modelAnimals_adj10, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.Animals10,list(c4th),by=NULL)
## contrast estimate SE df z.ratio
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -0.0212007 0.05613757 Inf -0.378
## p.value
## 0.7057
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
All models use normalized cognitive scores.Each model is adjusted for baseline age, sex, education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, and baseline PASE score
emm_options(rg.limit = 350000)
Age and Sex grouping
Tracking.data_long$Age_sex<-NA
Tracking.data_long$Age_sex[Tracking.data_long$Age<=64 & Tracking.data_long$Sex == "M"]<-"Males 45-64"
Tracking.data_long$Age_sex[Tracking.data_long$Age<=64 & Tracking.data_long$Sex == "F"]<-"Females 45-64"
Tracking.data_long$Age_sex[Tracking.data_long$Age>64 & Tracking.data_long$Sex == "M"]<-"Males 65+"
Tracking.data_long$Age_sex[Tracking.data_long$Age>64 & Tracking.data_long$Sex == "F"]<-"Females 65+"
modelRVLT_imm_11<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= Tracking.data_long)
summary(modelRVLT_imm_11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Immediate_Normed ~ timefactor * Pandemic * Age_sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 132211.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6817 -0.5799 -0.0467 0.5201 5.0159
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.253 2.292
## Residual 8.275 2.877
## Number of obs: 24887, groups: ID, 8467
##
## Fixed effects:
## Estimate
## (Intercept) 8.763e+00
## timefactor2 1.103e+00
## timefactor3 1.510e+00
## PandemicFU2 data collected before COVID-19 6.651e-01
## Age_sexFemales 65+ 1.288e+00
## Age_sexMales 45-64 -6.742e-01
## Age_sexMales 65+ 3.018e-01
## EducationHigh School Diploma 2.339e-01
## EducationLess than High School Diploma 7.360e-01
## EducationSome College 3.613e-01
## EthnicityWhite 9.758e-01
## IncomeLevel>$150k 8.914e-01
## IncomeLevel$100-150k 5.682e-01
## IncomeLevel$20-50k 9.433e-02
## IncomeLevel$50-100k 5.408e-01
## BMI -1.984e-02
## CESD.20.1 -3.635e-02
## SmokingStatusFormer Smoker 2.016e-01
## SmokingStatusNever Smoked 3.643e-01
## SmokingStatusOccasional Smoker 2.828e-01
## RelationshipstatusMarried 9.763e-02
## RelationshipstatusSeparated -1.258e-01
## RelationshipstatusSingle -9.473e-02
## RelationshipstatusWidowed -7.085e-02
## LivingstatusAssisted Living -5.967e-01
## LivingstatusHouse 1.083e-01
## LivingstatusOther -3.506e-01
## AnxietyYes -3.487e-02
## MoodDisordYes -1.287e-01
## Chronicconditions -3.283e-02
## PASE_TOTALbaseline 2.816e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -3.502e-01
## timefactor3:PandemicFU2 data collected before COVID-19 -4.879e-01
## timefactor2:Age_sexFemales 65+ -1.060e+00
## timefactor3:Age_sexFemales 65+ -1.554e+00
## timefactor2:Age_sexMales 45-64 -2.463e-01
## timefactor3:Age_sexMales 45-64 -1.447e-01
## timefactor2:Age_sexMales 65+ -9.250e-01
## timefactor3:Age_sexMales 65+ -1.347e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -5.781e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.249e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -4.640e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 5.078e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 5.834e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -7.213e-02
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.413e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3.911e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 7.493e-01
## Std. Error
## (Intercept) 3.410e-01
## timefactor2 1.237e-01
## timefactor3 1.254e-01
## PandemicFU2 data collected before COVID-19 1.425e-01
## Age_sexFemales 65+ 2.007e-01
## Age_sexMales 45-64 1.477e-01
## Age_sexMales 65+ 1.878e-01
## EducationHigh School Diploma 9.797e-02
## EducationLess than High School Diploma 1.384e-01
## EducationSome College 1.218e-01
## EthnicityWhite 1.941e-01
## IncomeLevel>$150k 1.819e-01
## IncomeLevel$100-150k 1.453e-01
## IncomeLevel$20-50k 9.692e-02
## IncomeLevel$50-100k 1.044e-01
## BMI 6.556e-03
## CESD.20.1 7.808e-03
## SmokingStatusFormer Smoker 1.334e-01
## SmokingStatusNever Smoked 1.389e-01
## SmokingStatusOccasional Smoker 2.629e-01
## RelationshipstatusMarried 1.139e-01
## RelationshipstatusSeparated 2.239e-01
## RelationshipstatusSingle 1.536e-01
## RelationshipstatusWidowed 1.549e-01
## LivingstatusAssisted Living 4.610e-01
## LivingstatusHouse 1.025e-01
## LivingstatusOther 3.824e-01
## AnxietyYes 1.366e-01
## MoodDisordYes 9.925e-02
## Chronicconditions 1.587e-02
## PASE_TOTALbaseline 4.518e-04
## timefactor2:PandemicFU2 data collected before COVID-19 1.582e-01
## timefactor3:PandemicFU2 data collected before COVID-19 1.596e-01
## timefactor2:Age_sexFemales 65+ 2.147e-01
## timefactor3:Age_sexFemales 65+ 2.178e-01
## timefactor2:Age_sexMales 45-64 1.614e-01
## timefactor3:Age_sexMales 45-64 1.629e-01
## timefactor2:Age_sexMales 65+ 2.048e-01
## timefactor3:Age_sexMales 65+ 2.081e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.459e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.022e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.390e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.733e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.759e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.251e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.260e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.662e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.686e-01
## df
## (Intercept) 9.137e+03
## timefactor2 1.647e+04
## timefactor3 1.660e+04
## PandemicFU2 data collected before COVID-19 1.906e+04
## Age_sexFemales 65+ 1.809e+04
## Age_sexMales 45-64 1.859e+04
## Age_sexMales 65+ 1.857e+04
## EducationHigh School Diploma 8.406e+03
## EducationLess than High School Diploma 8.467e+03
## EducationSome College 8.369e+03
## EthnicityWhite 8.364e+03
## IncomeLevel>$150k 8.405e+03
## IncomeLevel$100-150k 8.377e+03
## IncomeLevel$20-50k 8.409e+03
## IncomeLevel$50-100k 8.406e+03
## BMI 8.390e+03
## CESD.20.1 8.398e+03
## SmokingStatusFormer Smoker 8.406e+03
## SmokingStatusNever Smoked 8.406e+03
## SmokingStatusOccasional Smoker 8.354e+03
## RelationshipstatusMarried 8.414e+03
## RelationshipstatusSeparated 8.444e+03
## RelationshipstatusSingle 8.406e+03
## RelationshipstatusWidowed 8.421e+03
## LivingstatusAssisted Living 8.429e+03
## LivingstatusHouse 8.409e+03
## LivingstatusOther 8.346e+03
## AnxietyYes 8.398e+03
## MoodDisordYes 8.400e+03
## Chronicconditions 8.397e+03
## PASE_TOTALbaseline 8.392e+03
## timefactor2:PandemicFU2 data collected before COVID-19 1.646e+04
## timefactor3:PandemicFU2 data collected before COVID-19 1.655e+04
## timefactor2:Age_sexFemales 65+ 1.646e+04
## timefactor3:Age_sexFemales 65+ 1.659e+04
## timefactor2:Age_sexMales 45-64 1.647e+04
## timefactor3:Age_sexMales 45-64 1.656e+04
## timefactor2:Age_sexMales 65+ 1.647e+04
## timefactor3:Age_sexMales 65+ 1.662e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.906e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.911e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.911e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.647e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.656e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.647e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.651e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.647e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.656e+04
## t value
## (Intercept) 25.694
## timefactor2 8.914
## timefactor3 12.040
## PandemicFU2 data collected before COVID-19 4.668
## Age_sexFemales 65+ 6.419
## Age_sexMales 45-64 -4.564
## Age_sexMales 65+ 1.607
## EducationHigh School Diploma 2.387
## EducationLess than High School Diploma 5.318
## EducationSome College 2.966
## EthnicityWhite 5.027
## IncomeLevel>$150k 4.900
## IncomeLevel$100-150k 3.912
## IncomeLevel$20-50k 0.973
## IncomeLevel$50-100k 5.181
## BMI -3.025
## CESD.20.1 -4.655
## SmokingStatusFormer Smoker 1.512
## SmokingStatusNever Smoked 2.623
## SmokingStatusOccasional Smoker 1.076
## RelationshipstatusMarried 0.857
## RelationshipstatusSeparated -0.562
## RelationshipstatusSingle -0.617
## RelationshipstatusWidowed -0.457
## LivingstatusAssisted Living -1.294
## LivingstatusHouse 1.057
## LivingstatusOther -0.917
## AnxietyYes -0.255
## MoodDisordYes -1.297
## Chronicconditions -2.068
## PASE_TOTALbaseline 6.233
## timefactor2:PandemicFU2 data collected before COVID-19 -2.213
## timefactor3:PandemicFU2 data collected before COVID-19 -3.057
## timefactor2:Age_sexFemales 65+ -4.939
## timefactor3:Age_sexFemales 65+ -7.135
## timefactor2:Age_sexMales 45-64 -1.526
## timefactor3:Age_sexMales 45-64 -0.888
## timefactor2:Age_sexMales 65+ -4.516
## timefactor3:Age_sexMales 65+ -6.475
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -2.351
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.618
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.941
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.858
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.114
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.320
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.107
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.469
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.790
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 < 2e-16
## timefactor3 < 2e-16
## PandemicFU2 data collected before COVID-19 3.06e-06
## Age_sexFemales 65+ 1.40e-10
## Age_sexMales 45-64 5.06e-06
## Age_sexMales 65+ 0.10797
## EducationHigh School Diploma 0.01701
## EducationLess than High School Diploma 1.07e-07
## EducationSome College 0.00303
## EthnicityWhite 5.08e-07
## IncomeLevel>$150k 9.78e-07
## IncomeLevel$100-150k 9.24e-05
## IncomeLevel$20-50k 0.33043
## IncomeLevel$50-100k 2.26e-07
## BMI 0.00249
## CESD.20.1 3.28e-06
## SmokingStatusFormer Smoker 0.13062
## SmokingStatusNever Smoked 0.00873
## SmokingStatusOccasional Smoker 0.28206
## RelationshipstatusMarried 0.39148
## RelationshipstatusSeparated 0.57429
## RelationshipstatusSingle 0.53741
## RelationshipstatusWidowed 0.64741
## LivingstatusAssisted Living 0.19555
## LivingstatusHouse 0.29076
## LivingstatusOther 0.35918
## AnxietyYes 0.79856
## MoodDisordYes 0.19483
## Chronicconditions 0.03864
## PASE_TOTALbaseline 4.80e-10
## timefactor2:PandemicFU2 data collected before COVID-19 0.02688
## timefactor3:PandemicFU2 data collected before COVID-19 0.00224
## timefactor2:Age_sexFemales 65+ 7.93e-07
## timefactor3:Age_sexFemales 65+ 1.01e-12
## timefactor2:Age_sexMales 45-64 0.12698
## timefactor3:Age_sexMales 45-64 0.37442
## timefactor2:Age_sexMales 65+ 6.35e-06
## timefactor3:Age_sexMales 65+ 9.77e-11
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.01875
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.53685
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.05225
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.06316
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.03450
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.74863
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.91497
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.14183
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.00528
##
## (Intercept) ***
## timefactor2 ***
## timefactor3 ***
## PandemicFU2 data collected before COVID-19 ***
## Age_sexFemales 65+ ***
## Age_sexMales 45-64 ***
## Age_sexMales 65+
## EducationHigh School Diploma *
## EducationLess than High School Diploma ***
## EducationSome College **
## EthnicityWhite ***
## IncomeLevel>$150k ***
## IncomeLevel$100-150k ***
## IncomeLevel$20-50k
## IncomeLevel$50-100k ***
## BMI **
## CESD.20.1 ***
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked **
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed
## LivingstatusAssisted Living
## LivingstatusHouse
## LivingstatusOther
## AnxietyYes
## MoodDisordYes
## Chronicconditions *
## PASE_TOTALbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19 *
## timefactor3:PandemicFU2 data collected before COVID-19 **
## timefactor2:Age_sexFemales 65+ ***
## timefactor3:Age_sexFemales 65+ ***
## timefactor2:Age_sexMales 45-64
## timefactor3:Age_sexMales 45-64
## timefactor2:Age_sexMales 65+ ***
## timefactor3:Age_sexMales 65+ ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ *
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ .
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ .
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ *
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Significant differences for males 45-64 and 65+, whereby post-pandemic males performed worse
lsmeans(modelRVLT_imm_11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.561568 0.2322923 Inf 9.106283 10.016852
## FU2 data collected before COVID-19 10.226675 0.2232793 Inf 9.789056 10.664294
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.664144 0.2328179 Inf 10.207829 11.120459
## FU2 data collected before COVID-19 10.979022 0.2235560 Inf 10.540861 11.417184
##
## timefactor = 3, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.071726 0.2336883 Inf 10.613705 11.529746
## FU2 data collected before COVID-19 11.248962 0.2235464 Inf 10.810819 11.687104
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.849650 0.2593162 Inf 10.341399 11.357900
## FU2 data collected before COVID-19 10.936676 0.2424978 Inf 10.461389 11.411963
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.891887 0.2599483 Inf 10.382398 11.401377
## FU2 data collected before COVID-19 11.136442 0.2434508 Inf 10.659287 11.613597
##
## timefactor = 3, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.805592 0.2617093 Inf 10.292651 11.318533
## FU2 data collected before COVID-19 10.988098 0.2434786 Inf 10.510889 11.465307
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 8.887409 0.2229581 Inf 8.450419 9.324398
## FU2 data collected before COVID-19 9.677417 0.2310722 Inf 9.224524 10.130310
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.743717 0.2232954 Inf 9.306066 10.181368
## FU2 data collected before COVID-19 10.111368 0.2315836 Inf 9.657472 10.565263
##
## timefactor = 3, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.252856 0.2234918 Inf 9.814820 10.690892
## FU2 data collected before COVID-19 10.579126 0.2313256 Inf 10.125736 11.032516
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.863417 0.2549423 Inf 9.363740 10.363095
## FU2 data collected before COVID-19 10.064506 0.2407902 Inf 9.592566 10.536446
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.040998 0.2557090 Inf 9.539817 10.542178
## FU2 data collected before COVID-19 10.282929 0.2415629 Inf 9.809474 10.756383
##
## timefactor = 3, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.026294 0.2575882 Inf 9.521431 10.531158
## FU2 data collected before COVID-19 10.488830 0.2413928 Inf 10.015709 10.961951
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.6651072 0.1424855 Inf -4.668 <.0001
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3148784 0.1436699 Inf -2.192 0.0284
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1772359 0.1451827 Inf -1.221 0.2222
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0870258 0.2007446 Inf -0.434 0.6646
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2445549 0.2026868 Inf -1.207 0.2276
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1825060 0.2051738 Inf -0.890 0.3737
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.7900083 0.1440001 Inf -5.486 <.0001
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3676509 0.1454072 Inf -2.528 0.0115
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3262696 0.1453545 Inf -2.245 0.0248
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2010890 0.1921443 Inf -1.047 0.2953
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2419311 0.1941274 Inf -1.246 0.2127
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4625359 0.1962725 Inf -2.357 0.0184
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_imm_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.6651072 0.1424855 Inf -0.9443736 -0.3858408
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3148784 0.1436699 Inf -0.5964663 -0.0332906
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1772359 0.1451827 Inf -0.4617887 0.1073169
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0870258 0.2007446 Inf -0.4804780 0.3064264
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2445549 0.2026868 Inf -0.6418137 0.1527040
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1825060 0.2051738 Inf -0.5846393 0.2196273
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.7900083 0.1440001 Inf -1.0722433 -0.5077732
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3676509 0.1454072 Inf -0.6526438 -0.0826581
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3262696 0.1453545 Inf -0.6111591 -0.0413800
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2010890 0.1921443 Inf -0.5776850 0.1755070
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2419311 0.1941274 Inf -0.6224139 0.1385516
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4625359 0.1962725 Inf -0.8472230 -0.0778488
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTimmediate_lsmeans_11 <- summary(lsmeans(modelRVLT_imm_11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24887' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24887)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTimmediate_lsmeans_11$Time<-NA
RVLTimmediate_lsmeans_11$Time[RVLTimmediate_lsmeans_11$timefactor==1]<-"Baseline"
RVLTimmediate_lsmeans_11$Time[RVLTimmediate_lsmeans_11$timefactor==2]<-"Follow-up 1"
RVLTimmediate_lsmeans_11$Time[RVLTimmediate_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "RVLT Immediate Score", title = "RVLT Immediate Score from Baseline to FU2 by Pandemic status") +
theme_bw()
modelRVLT_del_11<- lmer(RVLT_Delayed_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= Tracking.data_long)
summary(modelRVLT_del_11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic * Age_sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 130355.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8353 -0.5638 -0.0391 0.5149 4.9227
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.552 2.356
## Residual 7.672 2.770
## Number of obs: 24749, groups: ID, 8467
##
## Fixed effects:
## Estimate
## (Intercept) 9.242e+00
## timefactor2 8.826e-01
## timefactor3 1.681e+00
## PandemicFU2 data collected before COVID-19 6.391e-01
## Age_sexFemales 65+ 1.421e+00
## Age_sexMales 45-64 -5.731e-01
## Age_sexMales 65+ 6.437e-01
## EducationHigh School Diploma 4.089e-01
## EducationLess than High School Diploma 6.389e-01
## EducationSome College 4.473e-01
## EthnicityWhite 9.189e-01
## IncomeLevel>$150k 7.148e-01
## IncomeLevel$100-150k 6.172e-01
## IncomeLevel$20-50k 1.905e-01
## IncomeLevel$50-100k 5.736e-01
## BMI -1.910e-02
## CESD.20.1 -3.615e-02
## SmokingStatusFormer Smoker -2.425e-02
## SmokingStatusNever Smoked 2.280e-01
## SmokingStatusOccasional Smoker 1.572e-01
## RelationshipstatusMarried -1.475e-02
## RelationshipstatusSeparated -6.993e-02
## RelationshipstatusSingle -1.843e-01
## RelationshipstatusWidowed -8.461e-02
## LivingstatusAssisted Living -1.052e+00
## LivingstatusHouse 7.402e-02
## LivingstatusOther -8.581e-02
## AnxietyYes -1.013e-02
## MoodDisordYes -1.325e-01
## Chronicconditions -4.222e-02
## PASE_TOTALbaseline 2.808e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -1.926e-01
## timefactor3:PandemicFU2 data collected before COVID-19 -5.858e-01
## timefactor2:Age_sexFemales 65+ -9.016e-01
## timefactor3:Age_sexFemales 65+ -1.519e+00
## timefactor2:Age_sexMales 45-64 -1.369e-01
## timefactor3:Age_sexMales 45-64 -2.207e-01
## timefactor2:Age_sexMales 65+ -8.686e-01
## timefactor3:Age_sexMales 65+ -1.455e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -6.001e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.234e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -6.464e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 5.637e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 8.513e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -9.969e-02
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -3.217e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 4.714e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 8.783e-01
## Std. Error
## (Intercept) 3.431e-01
## timefactor2 1.194e-01
## timefactor3 1.208e-01
## PandemicFU2 data collected before COVID-19 1.409e-01
## Age_sexFemales 65+ 1.987e-01
## Age_sexMales 45-64 1.462e-01
## Age_sexMales 65+ 1.858e-01
## EducationHigh School Diploma 9.865e-02
## EducationLess than High School Diploma 1.395e-01
## EducationSome College 1.226e-01
## EthnicityWhite 1.960e-01
## IncomeLevel>$150k 1.831e-01
## IncomeLevel$100-150k 1.463e-01
## IncomeLevel$20-50k 9.760e-02
## IncomeLevel$50-100k 1.051e-01
## BMI 6.601e-03
## CESD.20.1 7.866e-03
## SmokingStatusFormer Smoker 1.342e-01
## SmokingStatusNever Smoked 1.398e-01
## SmokingStatusOccasional Smoker 2.649e-01
## RelationshipstatusMarried 1.147e-01
## RelationshipstatusSeparated 2.256e-01
## RelationshipstatusSingle 1.546e-01
## RelationshipstatusWidowed 1.560e-01
## LivingstatusAssisted Living 4.644e-01
## LivingstatusHouse 1.033e-01
## LivingstatusOther 3.855e-01
## AnxietyYes 1.376e-01
## MoodDisordYes 9.991e-02
## Chronicconditions 1.599e-02
## PASE_TOTALbaseline 4.551e-04
## timefactor2:PandemicFU2 data collected before COVID-19 1.527e-01
## timefactor3:PandemicFU2 data collected before COVID-19 1.538e-01
## timefactor2:Age_sexFemales 65+ 2.072e-01
## timefactor3:Age_sexFemales 65+ 2.110e-01
## timefactor2:Age_sexMales 45-64 1.559e-01
## timefactor3:Age_sexMales 45-64 1.570e-01
## timefactor2:Age_sexMales 65+ 1.983e-01
## timefactor3:Age_sexMales 65+ 2.012e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.432e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.000e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.363e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.636e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.669e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.172e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.180e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.577e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.596e-01
## df
## (Intercept) 9.099e+03
## timefactor2 1.636e+04
## timefactor3 1.646e+04
## PandemicFU2 data collected before COVID-19 1.828e+04
## Age_sexFemales 65+ 1.735e+04
## Age_sexMales 45-64 1.783e+04
## Age_sexMales 65+ 1.781e+04
## EducationHigh School Diploma 8.407e+03
## EducationLess than High School Diploma 8.507e+03
## EducationSome College 8.365e+03
## EthnicityWhite 8.443e+03
## IncomeLevel>$150k 8.397e+03
## IncomeLevel$100-150k 8.381e+03
## IncomeLevel$20-50k 8.415e+03
## IncomeLevel$50-100k 8.405e+03
## BMI 8.386e+03
## CESD.20.1 8.412e+03
## SmokingStatusFormer Smoker 8.397e+03
## SmokingStatusNever Smoked 8.397e+03
## SmokingStatusOccasional Smoker 8.373e+03
## RelationshipstatusMarried 8.413e+03
## RelationshipstatusSeparated 8.455e+03
## RelationshipstatusSingle 8.398e+03
## RelationshipstatusWidowed 8.420e+03
## LivingstatusAssisted Living 8.446e+03
## LivingstatusHouse 8.420e+03
## LivingstatusOther 8.382e+03
## AnxietyYes 8.396e+03
## MoodDisordYes 8.394e+03
## Chronicconditions 8.401e+03
## PASE_TOTALbaseline 8.400e+03
## timefactor2:PandemicFU2 data collected before COVID-19 1.635e+04
## timefactor3:PandemicFU2 data collected before COVID-19 1.641e+04
## timefactor2:Age_sexFemales 65+ 1.634e+04
## timefactor3:Age_sexFemales 65+ 1.650e+04
## timefactor2:Age_sexMales 45-64 1.637e+04
## timefactor3:Age_sexMales 45-64 1.643e+04
## timefactor2:Age_sexMales 65+ 1.638e+04
## timefactor3:Age_sexMales 65+ 1.651e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.828e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.833e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.833e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.635e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.646e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.636e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.639e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.639e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.645e+04
## t value
## (Intercept) 26.939
## timefactor2 7.391
## timefactor3 13.917
## PandemicFU2 data collected before COVID-19 4.536
## Age_sexFemales 65+ 7.151
## Age_sexMales 45-64 -3.921
## Age_sexMales 65+ 3.465
## EducationHigh School Diploma 4.145
## EducationLess than High School Diploma 4.578
## EducationSome College 3.648
## EthnicityWhite 4.688
## IncomeLevel>$150k 3.903
## IncomeLevel$100-150k 4.219
## IncomeLevel$20-50k 1.952
## IncomeLevel$50-100k 5.458
## BMI -2.893
## CESD.20.1 -4.595
## SmokingStatusFormer Smoker -0.181
## SmokingStatusNever Smoked 1.631
## SmokingStatusOccasional Smoker 0.593
## RelationshipstatusMarried -0.129
## RelationshipstatusSeparated -0.310
## RelationshipstatusSingle -1.192
## RelationshipstatusWidowed -0.542
## LivingstatusAssisted Living -2.265
## LivingstatusHouse 0.717
## LivingstatusOther -0.223
## AnxietyYes -0.074
## MoodDisordYes -1.326
## Chronicconditions -2.641
## PASE_TOTALbaseline 6.172
## timefactor2:PandemicFU2 data collected before COVID-19 -1.262
## timefactor3:PandemicFU2 data collected before COVID-19 -3.809
## timefactor2:Age_sexFemales 65+ -4.352
## timefactor3:Age_sexFemales 65+ -7.197
## timefactor2:Age_sexMales 45-64 -0.878
## timefactor3:Age_sexMales 45-64 -1.405
## timefactor2:Age_sexMales 65+ -4.380
## timefactor3:Age_sexMales 65+ -7.232
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -2.468
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.617
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -2.735
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.138
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 3.189
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.459
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.148
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.830
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3.383
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 1.53e-13
## timefactor3 < 2e-16
## PandemicFU2 data collected before COVID-19 5.77e-06
## Age_sexFemales 65+ 8.93e-13
## Age_sexMales 45-64 8.86e-05
## Age_sexMales 65+ 0.000532
## EducationHigh School Diploma 3.43e-05
## EducationLess than High School Diploma 4.76e-06
## EducationSome College 0.000266
## EthnicityWhite 2.80e-06
## IncomeLevel>$150k 9.56e-05
## IncomeLevel$100-150k 2.47e-05
## IncomeLevel$20-50k 0.050935
## IncomeLevel$50-100k 4.94e-08
## BMI 0.003825
## CESD.20.1 4.38e-06
## SmokingStatusFormer Smoker 0.856672
## SmokingStatusNever Smoked 0.102964
## SmokingStatusOccasional Smoker 0.552875
## RelationshipstatusMarried 0.897699
## RelationshipstatusSeparated 0.756557
## RelationshipstatusSingle 0.233345
## RelationshipstatusWidowed 0.587536
## LivingstatusAssisted Living 0.023564
## LivingstatusHouse 0.473589
## LivingstatusOther 0.823844
## AnxietyYes 0.941308
## MoodDisordYes 0.184987
## Chronicconditions 0.008283
## PASE_TOTALbaseline 7.06e-10
## timefactor2:PandemicFU2 data collected before COVID-19 0.206990
## timefactor3:PandemicFU2 data collected before COVID-19 0.000140
## timefactor2:Age_sexFemales 65+ 1.36e-05
## timefactor3:Age_sexFemales 65+ 6.43e-13
## timefactor2:Age_sexMales 45-64 0.379697
## timefactor3:Age_sexMales 45-64 0.159992
## timefactor2:Age_sexMales 65+ 1.19e-05
## timefactor3:Age_sexMales 65+ 4.97e-13
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.013597
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.537085
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.006242
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.032505
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.001428
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.646282
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.882687
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.067313
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.000718
##
## (Intercept) ***
## timefactor2 ***
## timefactor3 ***
## PandemicFU2 data collected before COVID-19 ***
## Age_sexFemales 65+ ***
## Age_sexMales 45-64 ***
## Age_sexMales 65+ ***
## EducationHigh School Diploma ***
## EducationLess than High School Diploma ***
## EducationSome College ***
## EthnicityWhite ***
## IncomeLevel>$150k ***
## IncomeLevel$100-150k ***
## IncomeLevel$20-50k .
## IncomeLevel$50-100k ***
## BMI **
## CESD.20.1 ***
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed
## LivingstatusAssisted Living *
## LivingstatusHouse
## LivingstatusOther
## AnxietyYes
## MoodDisordYes
## Chronicconditions **
## PASE_TOTALbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19
## timefactor3:PandemicFU2 data collected before COVID-19 ***
## timefactor2:Age_sexFemales 65+ ***
## timefactor3:Age_sexFemales 65+ ***
## timefactor2:Age_sexMales 45-64
## timefactor3:Age_sexMales 45-64
## timefactor2:Age_sexMales 65+ ***
## timefactor3:Age_sexMales 65+ ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ *
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ **
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ *
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ **
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ .
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_del_11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.849428 0.2330947 Inf 9.392571 10.306286
## FU2 data collected before COVID-19 10.488492 0.2243536 Inf 10.048767 10.928217
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.732000 0.2338147 Inf 10.273732 11.190269
## FU2 data collected before COVID-19 11.178426 0.2246642 Inf 10.738092 11.618760
##
## timefactor = 3, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.530816 0.2343965 Inf 11.071407 11.990225
## FU2 data collected before COVID-19 11.584072 0.2246735 Inf 11.143720 12.024424
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.270153 0.2595151 Inf 10.761513 11.778793
## FU2 data collected before COVID-19 11.309111 0.2432116 Inf 10.832425 11.785797
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.251169 0.2602680 Inf 10.741054 11.761285
## FU2 data collected before COVID-19 11.661179 0.2442304 Inf 11.182497 12.139862
##
## timefactor = 3, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.432781 0.2628893 Inf 10.917527 11.948034
## FU2 data collected before COVID-19 11.737278 0.2443392 Inf 11.258382 12.216174
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.276376 0.2239543 Inf 8.837434 9.715319
## FU2 data collected before COVID-19 10.038862 0.2318877 Inf 9.584370 10.493353
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.022024 0.2244885 Inf 9.582034 10.462013
## FU2 data collected before COVID-19 10.492178 0.2323855 Inf 10.036710 10.947645
##
## timefactor = 3, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.737100 0.2245403 Inf 10.297009 11.177191
## FU2 data collected before COVID-19 10.881612 0.2323218 Inf 10.426270 11.336955
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.493155 0.2553214 Inf 9.992735 10.993576
## FU2 data collected before COVID-19 10.485784 0.2414561 Inf 10.012539 10.959029
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.507100 0.2567392 Inf 10.003900 11.010299
## FU2 data collected before COVID-19 10.778528 0.2427441 Inf 10.302758 11.254297
##
## timefactor = 3, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.719691 0.2584393 Inf 10.213159 11.226223
## FU2 data collected before COVID-19 11.004843 0.2423233 Inf 10.529898 11.479788
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.6390634 0.1408875 Inf -4.536 <.0001
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4464254 0.1423107 Inf -3.137 0.0017
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0532563 0.1435597 Inf -0.371 0.7107
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0389579 0.1985069 Inf -0.196 0.8444
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4100099 0.2007451 Inf -2.042 0.0411
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3044979 0.2041795 Inf -1.491 0.1359
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.7624855 0.1423852 Inf -5.355 <.0001
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4701538 0.1441308 Inf -3.262 0.0011
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1445124 0.1440377 Inf -1.003 0.3157
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.0073714 0.1899777 Inf 0.039 0.9690
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2714280 0.1934002 Inf -1.403 0.1605
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2851517 0.1950666 Inf -1.462 0.1438
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_del_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.6390634 0.1408875 Inf -0.9151979 -0.3629290
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4464254 0.1423107 Inf -0.7253492 -0.1675016
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0532563 0.1435597 Inf -0.3346282 0.2281156
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0389579 0.1985069 Inf -0.4280242 0.3501084
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4100099 0.2007451 Inf -0.8034630 -0.0165568
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3044979 0.2041795 Inf -0.7046824 0.0956865
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.7624855 0.1423852 Inf -1.0415553 -0.4834157
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4701538 0.1441308 Inf -0.7526448 -0.1876627
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1445124 0.1440377 Inf -0.4268212 0.1377963
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.0073714 0.1899777 Inf -0.3649781 0.3797209
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2714280 0.1934002 Inf -0.6504854 0.1076293
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2851517 0.1950666 Inf -0.6674752 0.0971717
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTdelayed_lsmeans_11 <- summary(lsmeans(modelRVLT_del_11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 24749' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 24749)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTdelayed_lsmeans_11$Time<-NA
RVLTdelayed_lsmeans_11$Time[RVLTdelayed_lsmeans_11$timefactor==1]<-"Baseline"
RVLTdelayed_lsmeans_11$Time[RVLTdelayed_lsmeans_11$timefactor==2]<-"Follow-up 1"
RVLTdelayed_lsmeans_11$Time[RVLTdelayed_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "RVLT Delayed Score", title = "RVLT Delayed Score from Baseline to FU2 by Pandemic status") +
theme_bw()
modelMAT_11<- lmer(MAT_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= Tracking.data_long)
summary(modelMAT_11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## MAT_Normed ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +
## IncomeLevel + BMI + CESD.20.1 + SmokingStatus + Relationshipstatus +
## Livingstatus + Anxiety + MoodDisord + Chronicconditions +
## PASE_TOTALbaseline + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 123827.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0997 -0.4758 0.0038 0.4500 4.6476
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.090 2.256
## Residual 7.157 2.675
## Number of obs: 23820, groups: ID, 8467
##
## Fixed effects:
## Estimate
## (Intercept) 9.443e+00
## timefactor2 2.951e+00
## timefactor3 -2.981e-01
## PandemicFU2 data collected before COVID-19 8.394e-02
## Age_sexFemales 65+ 2.050e-01
## Age_sexMales 45-64 -4.177e-01
## Age_sexMales 65+ -5.695e-01
## EducationHigh School Diploma -6.593e-03
## EducationLess than High School Diploma 1.550e-02
## EducationSome College 4.530e-02
## EthnicityWhite 1.318e+00
## IncomeLevel>$150k 9.046e-01
## IncomeLevel$100-150k 9.814e-01
## IncomeLevel$20-50k 3.624e-01
## IncomeLevel$50-100k 7.254e-01
## BMI -2.543e-02
## CESD.20.1 -4.506e-02
## SmokingStatusFormer Smoker 2.002e-01
## SmokingStatusNever Smoked 1.502e-01
## SmokingStatusOccasional Smoker 1.558e-01
## RelationshipstatusMarried 1.714e-01
## RelationshipstatusSeparated -1.933e-01
## RelationshipstatusSingle 2.749e-01
## RelationshipstatusWidowed -1.197e-01
## LivingstatusAssisted Living -7.241e-01
## LivingstatusHouse -1.815e-01
## LivingstatusOther -4.010e-01
## AnxietyYes 8.022e-02
## MoodDisordYes 2.640e-01
## Chronicconditions -5.850e-02
## PASE_TOTALbaseline -1.046e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -1.545e+00
## timefactor3:PandemicFU2 data collected before COVID-19 1.004e-01
## timefactor2:Age_sexFemales 65+ -7.842e-01
## timefactor3:Age_sexFemales 65+ -2.857e-01
## timefactor2:Age_sexMales 45-64 -3.091e+00
## timefactor3:Age_sexMales 45-64 2.006e-01
## timefactor2:Age_sexMales 65+ -3.046e+00
## timefactor3:Age_sexMales 65+ -1.617e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -1.458e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 4.203e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3.284e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.144e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 3.226e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.598e+00
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.611e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.616e+00
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.453e-02
## Std. Error
## (Intercept) 3.321e-01
## timefactor2 1.170e-01
## timefactor3 1.180e-01
## PandemicFU2 data collected before COVID-19 1.356e-01
## Age_sexFemales 65+ 1.913e-01
## Age_sexMales 45-64 1.407e-01
## Age_sexMales 65+ 1.789e-01
## EducationHigh School Diploma 9.556e-02
## EducationLess than High School Diploma 1.357e-01
## EducationSome College 1.185e-01
## EthnicityWhite 1.900e-01
## IncomeLevel>$150k 1.773e-01
## IncomeLevel$100-150k 1.415e-01
## IncomeLevel$20-50k 9.461e-02
## IncomeLevel$50-100k 1.018e-01
## BMI 6.382e-03
## CESD.20.1 7.610e-03
## SmokingStatusFormer Smoker 1.299e-01
## SmokingStatusNever Smoked 1.352e-01
## SmokingStatusOccasional Smoker 2.563e-01
## RelationshipstatusMarried 1.111e-01
## RelationshipstatusSeparated 2.181e-01
## RelationshipstatusSingle 1.497e-01
## RelationshipstatusWidowed 1.513e-01
## LivingstatusAssisted Living 4.499e-01
## LivingstatusHouse 1.001e-01
## LivingstatusOther 3.740e-01
## AnxietyYes 1.330e-01
## MoodDisordYes 9.661e-02
## Chronicconditions 1.550e-02
## PASE_TOTALbaseline 4.401e-04
## timefactor2:PandemicFU2 data collected before COVID-19 1.496e-01
## timefactor3:PandemicFU2 data collected before COVID-19 1.502e-01
## timefactor2:Age_sexFemales 65+ 2.067e-01
## timefactor3:Age_sexFemales 65+ 2.087e-01
## timefactor2:Age_sexMales 45-64 1.530e-01
## timefactor3:Age_sexMales 45-64 1.536e-01
## timefactor2:Age_sexMales 65+ 1.969e-01
## timefactor3:Age_sexMales 65+ 2.008e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.340e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.924e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.275e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.616e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.636e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.134e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.133e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.551e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.581e-01
## df
## (Intercept) 9.038e+03
## timefactor2 1.557e+04
## timefactor3 1.563e+04
## PandemicFU2 data collected before COVID-19 1.786e+04
## Age_sexFemales 65+ 1.697e+04
## Age_sexMales 45-64 1.742e+04
## Age_sexMales 65+ 1.740e+04
## EducationHigh School Diploma 8.362e+03
## EducationLess than High School Diploma 8.554e+03
## EducationSome College 8.271e+03
## EthnicityWhite 8.432e+03
## IncomeLevel>$150k 8.340e+03
## IncomeLevel$100-150k 8.311e+03
## IncomeLevel$20-50k 8.390e+03
## IncomeLevel$50-100k 8.368e+03
## BMI 8.295e+03
## CESD.20.1 8.332e+03
## SmokingStatusFormer Smoker 8.313e+03
## SmokingStatusNever Smoked 8.312e+03
## SmokingStatusOccasional Smoker 8.293e+03
## RelationshipstatusMarried 8.377e+03
## RelationshipstatusSeparated 8.367e+03
## RelationshipstatusSingle 8.355e+03
## RelationshipstatusWidowed 8.419e+03
## LivingstatusAssisted Living 8.385e+03
## LivingstatusHouse 8.398e+03
## LivingstatusOther 8.384e+03
## AnxietyYes 8.312e+03
## MoodDisordYes 8.304e+03
## Chronicconditions 8.384e+03
## PASE_TOTALbaseline 8.314e+03
## timefactor2:PandemicFU2 data collected before COVID-19 1.555e+04
## timefactor3:PandemicFU2 data collected before COVID-19 1.558e+04
## timefactor2:Age_sexFemales 65+ 1.573e+04
## timefactor3:Age_sexFemales 65+ 1.580e+04
## timefactor2:Age_sexMales 45-64 1.559e+04
## timefactor3:Age_sexMales 45-64 1.562e+04
## timefactor2:Age_sexMales 65+ 1.576e+04
## timefactor3:Age_sexMales 65+ 1.591e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.786e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.790e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.791e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.568e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.574e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.558e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.557e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.572e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.581e+04
## t value
## (Intercept) 28.433
## timefactor2 25.212
## timefactor3 -2.527
## PandemicFU2 data collected before COVID-19 0.619
## Age_sexFemales 65+ 1.071
## Age_sexMales 45-64 -2.969
## Age_sexMales 65+ -3.184
## EducationHigh School Diploma -0.069
## EducationLess than High School Diploma 0.114
## EducationSome College 0.382
## EthnicityWhite 6.933
## IncomeLevel>$150k 5.103
## IncomeLevel$100-150k 6.935
## IncomeLevel$20-50k 3.830
## IncomeLevel$50-100k 7.125
## BMI -3.985
## CESD.20.1 -5.921
## SmokingStatusFormer Smoker 1.542
## SmokingStatusNever Smoked 1.111
## SmokingStatusOccasional Smoker 0.608
## RelationshipstatusMarried 1.542
## RelationshipstatusSeparated -0.886
## RelationshipstatusSingle 1.836
## RelationshipstatusWidowed -0.791
## LivingstatusAssisted Living -1.610
## LivingstatusHouse -1.813
## LivingstatusOther -1.072
## AnxietyYes 0.603
## MoodDisordYes 2.733
## Chronicconditions -3.774
## PASE_TOTALbaseline -2.377
## timefactor2:PandemicFU2 data collected before COVID-19 -10.332
## timefactor3:PandemicFU2 data collected before COVID-19 0.668
## timefactor2:Age_sexFemales 65+ -3.793
## timefactor3:Age_sexFemales 65+ -1.369
## timefactor2:Age_sexMales 45-64 -20.201
## timefactor3:Age_sexMales 45-64 1.306
## timefactor2:Age_sexMales 65+ -15.468
## timefactor3:Age_sexMales 65+ -0.805
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -0.623
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.218
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.444
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.437
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.224
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 7.487
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.755
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 6.332
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.095
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 < 2e-16
## timefactor3 0.011520
## PandemicFU2 data collected before COVID-19 0.535900
## Age_sexFemales 65+ 0.283960
## Age_sexMales 45-64 0.002994
## Age_sexMales 65+ 0.001455
## EducationHigh School Diploma 0.944999
## EducationLess than High School Diploma 0.909086
## EducationSome College 0.702304
## EthnicityWhite 4.44e-12
## IncomeLevel>$150k 3.42e-07
## IncomeLevel$100-150k 4.37e-12
## IncomeLevel$20-50k 0.000129
## IncomeLevel$50-100k 1.13e-12
## BMI 6.82e-05
## CESD.20.1 3.33e-09
## SmokingStatusFormer Smoker 0.123099
## SmokingStatusNever Smoked 0.266557
## SmokingStatusOccasional Smoker 0.543454
## RelationshipstatusMarried 0.123066
## RelationshipstatusSeparated 0.375527
## RelationshipstatusSingle 0.066416
## RelationshipstatusWidowed 0.428945
## LivingstatusAssisted Living 0.107522
## LivingstatusHouse 0.069933
## LivingstatusOther 0.283704
## AnxietyYes 0.546504
## MoodDisordYes 0.006290
## Chronicconditions 0.000162
## PASE_TOTALbaseline 0.017477
## timefactor2:PandemicFU2 data collected before COVID-19 < 2e-16
## timefactor3:PandemicFU2 data collected before COVID-19 0.503906
## timefactor2:Age_sexFemales 65+ 0.000149
## timefactor3:Age_sexFemales 65+ 0.171048
## timefactor2:Age_sexMales 45-64 < 2e-16
## timefactor3:Age_sexMales 45-64 0.191435
## timefactor2:Age_sexMales 65+ < 2e-16
## timefactor3:Age_sexMales 65+ 0.420633
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.533193
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.827117
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.148849
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.661852
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.221045
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 7.40e-14
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.450039
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.48e-10
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.924286
##
## (Intercept) ***
## timefactor2 ***
## timefactor3 *
## PandemicFU2 data collected before COVID-19
## Age_sexFemales 65+
## Age_sexMales 45-64 **
## Age_sexMales 65+ **
## EducationHigh School Diploma
## EducationLess than High School Diploma
## EducationSome College
## EthnicityWhite ***
## IncomeLevel>$150k ***
## IncomeLevel$100-150k ***
## IncomeLevel$20-50k ***
## IncomeLevel$50-100k ***
## BMI ***
## CESD.20.1 ***
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle .
## RelationshipstatusWidowed
## LivingstatusAssisted Living
## LivingstatusHouse .
## LivingstatusOther
## AnxietyYes
## MoodDisordYes **
## Chronicconditions ***
## PASE_TOTALbaseline *
## timefactor2:PandemicFU2 data collected before COVID-19 ***
## timefactor3:PandemicFU2 data collected before COVID-19
## timefactor2:Age_sexFemales 65+ ***
## timefactor3:Age_sexFemales 65+
## timefactor2:Age_sexMales 45-64 ***
## timefactor3:Age_sexMales 45-64
## timefactor2:Age_sexMales 65+ ***
## timefactor3:Age_sexMales 65+
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 ***
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ ***
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Significant differences for males 65+
lsmeans(modelMAT_11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.463475 0.2255691 Inf 9.021367 9.905582
## FU2 data collected before COVID-19 9.547411 0.2172108 Inf 9.121686 9.973136
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 12.414464 0.2271972 Inf 11.969165 12.859762
## FU2 data collected before COVID-19 10.952979 0.2181102 Inf 10.525491 11.380467
##
## timefactor = 3, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.165364 0.2274768 Inf 8.719518 9.611210
## FU2 data collected before COVID-19 9.349674 0.2179146 Inf 8.922569 9.776779
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.668448 0.2509308 Inf 9.176633 10.160263
## FU2 data collected before COVID-19 9.606555 0.2353552 Inf 9.145267 10.067842
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.835241 0.2562129 Inf 11.333073 12.337409
## FU2 data collected before COVID-19 10.342360 0.2376782 Inf 9.876519 10.808201
##
## timefactor = 3, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.084621 0.2569091 Inf 8.581088 9.588154
## FU2 data collected before COVID-19 9.445706 0.2380809 Inf 8.979076 9.912336
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.045802 0.2167912 Inf 8.620899 9.470705
## FU2 data collected before COVID-19 9.171767 0.2244012 Inf 8.731949 9.611586
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 8.905959 0.2181591 Inf 8.478375 9.333543
## FU2 data collected before COVID-19 9.084069 0.2261540 Inf 8.640815 9.527323
##
## timefactor = 3, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 8.948295 0.2179078 Inf 8.521204 9.375387
## FU2 data collected before COVID-19 9.013503 0.2257825 Inf 8.570977 9.456029
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 8.894001 0.2469340 Inf 8.410019 9.377983
## FU2 data collected before COVID-19 9.306310 0.2336362 Inf 8.848392 9.764229
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 8.798722 0.2519857 Inf 8.304839 9.292605
## FU2 data collected before COVID-19 9.281193 0.2366219 Inf 8.817423 9.744964
##
## timefactor = 3, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 8.434179 0.2543076 Inf 7.935745 8.932612
## FU2 data collected before COVID-19 8.971393 0.2367166 Inf 8.507437 9.435349
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0839362 0.1355916 Inf -0.619 0.5359
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 1.4614851 0.1392757 Inf 10.493 <.0001
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1843099 0.1399270 Inf -1.317 0.1878
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.0618933 0.1910534 Inf 0.324 0.7460
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 1.4928811 0.2008567 Inf 7.433 <.0001
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3610846 0.2029033 Inf -1.780 0.0751
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1259655 0.1370328 Inf -0.919 0.3580
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1781102 0.1418371 Inf -1.256 0.2092
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0652074 0.1411178 Inf -0.462 0.6440
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4123094 0.1828342 Inf -2.255 0.0241
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4824717 0.1928114 Inf -2.502 0.0123
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.5372147 0.1962695 Inf -2.737 0.0062
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelMAT_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0839362 0.1355916 Inf -0.3496908 0.1818183
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 1.4614851 0.1392757 Inf 1.1885098 1.7344604
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1843099 0.1399270 Inf -0.4585617 0.0899419
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.0618933 0.1910534 Inf -0.3125645 0.4363512
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 1.4928811 0.2008567 Inf 1.0992091 1.8865530
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3610846 0.2029033 Inf -0.7587678 0.0365986
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1259655 0.1370328 Inf -0.3945449 0.1426138
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1781102 0.1418371 Inf -0.4561058 0.0998854
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0652074 0.1411178 Inf -0.3417932 0.2113783
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4123094 0.1828342 Inf -0.7706579 -0.0539610
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4824717 0.1928114 Inf -0.8603751 -0.1045683
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.5372147 0.1962695 Inf -0.9218958 -0.1525337
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
MAT_lsmeans_11 <- summary(lsmeans(modelMAT_11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 23820' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 23820)' or larger];
## but be warned that this may result in large computation time and memory use.
MAT_lsmeans_11$Time<-NA
MAT_lsmeans_11$Time[MAT_lsmeans_11$timefactor==1]<-"Baseline"
MAT_lsmeans_11$Time[MAT_lsmeans_11$timefactor==2]<-"Follow-up 1"
MAT_lsmeans_11$Time[MAT_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(MAT_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "MAT Score", title = "Mental Alteration Test Score from Baseline to FU2 by Pandemic status") +
theme_bw()
modelAnimals_11<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= Tracking.data_long)
summary(modelAnimals_11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Animal_Fluency_Normed ~ timefactor * Pandemic * Age_sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 116884.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3041 -0.5635 -0.0137 0.5488 4.7736
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.916 2.217
## Residual 3.618 1.902
## Number of obs: 25010, groups: ID, 8467
##
## Fixed effects:
## Estimate
## (Intercept) 8.850e+00
## timefactor2 -5.901e-02
## timefactor3 2.526e-01
## PandemicFU2 data collected before COVID-19 2.614e-01
## Age_sexFemales 65+ 2.563e-01
## Age_sexMales 45-64 -8.441e-02
## Age_sexMales 65+ -1.303e-01
## EducationHigh School Diploma 3.040e-01
## EducationLess than High School Diploma 4.347e-01
## EducationSome College 2.537e-01
## EthnicityWhite 1.314e+00
## IncomeLevel>$150k 6.146e-01
## IncomeLevel$100-150k 5.214e-01
## IncomeLevel$20-50k 8.887e-02
## IncomeLevel$50-100k 3.397e-01
## BMI -1.682e-02
## CESD.20.1 -3.603e-02
## SmokingStatusFormer Smoker 2.208e-01
## SmokingStatusNever Smoked 2.246e-01
## SmokingStatusOccasional Smoker 2.834e-01
## RelationshipstatusMarried -1.833e-01
## RelationshipstatusSeparated -1.667e-01
## RelationshipstatusSingle -9.008e-02
## RelationshipstatusWidowed -1.894e-01
## LivingstatusAssisted Living -1.139e-01
## LivingstatusHouse 3.327e-01
## LivingstatusOther 1.223e-01
## AnxietyYes -7.006e-02
## MoodDisordYes 1.948e-01
## Chronicconditions -1.098e-02
## PASE_TOTALbaseline 7.644e-04
## timefactor2:PandemicFU2 data collected before COVID-19 1.302e-01
## timefactor3:PandemicFU2 data collected before COVID-19 2.521e-02
## timefactor2:Age_sexFemales 65+ -3.627e-01
## timefactor3:Age_sexFemales 65+ -7.586e-01
## timefactor2:Age_sexMales 45-64 -2.931e-02
## timefactor3:Age_sexMales 45-64 -2.189e-01
## timefactor2:Age_sexMales 65+ -1.239e-01
## timefactor3:Age_sexMales 65+ -6.604e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -9.044e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.666e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.117e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 6.846e-02
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.871e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.727e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.783e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.620e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.080e-01
## Std. Error
## (Intercept) 2.948e-01
## timefactor2 8.179e-02
## timefactor3 8.281e-02
## PandemicFU2 data collected before COVID-19 1.132e-01
## Age_sexFemales 65+ 1.606e-01
## Age_sexMales 45-64 1.178e-01
## Age_sexMales 65+ 1.498e-01
## EducationHigh School Diploma 8.544e-02
## EducationLess than High School Diploma 1.205e-01
## EducationSome College 1.063e-01
## EthnicityWhite 1.693e-01
## IncomeLevel>$150k 1.586e-01
## IncomeLevel$100-150k 1.267e-01
## IncomeLevel$20-50k 8.449e-02
## IncomeLevel$50-100k 9.100e-02
## BMI 5.717e-03
## CESD.20.1 6.810e-03
## SmokingStatusFormer Smoker 1.163e-01
## SmokingStatusNever Smoked 1.211e-01
## SmokingStatusOccasional Smoker 2.295e-01
## RelationshipstatusMarried 9.935e-02
## RelationshipstatusSeparated 1.951e-01
## RelationshipstatusSingle 1.339e-01
## RelationshipstatusWidowed 1.350e-01
## LivingstatusAssisted Living 4.016e-01
## LivingstatusHouse 8.942e-02
## LivingstatusOther 3.334e-01
## AnxietyYes 1.191e-01
## MoodDisordYes 8.655e-02
## Chronicconditions 1.384e-02
## PASE_TOTALbaseline 3.940e-04
## timefactor2:PandemicFU2 data collected before COVID-19 1.046e-01
## timefactor3:PandemicFU2 data collected before COVID-19 1.054e-01
## timefactor2:Age_sexFemales 65+ 1.416e-01
## timefactor3:Age_sexFemales 65+ 1.438e-01
## timefactor2:Age_sexMales 45-64 1.067e-01
## timefactor3:Age_sexMales 45-64 1.077e-01
## timefactor2:Age_sexMales 65+ 1.351e-01
## timefactor3:Age_sexMales 65+ 1.373e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.954e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.607e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.899e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.801e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.820e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.487e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.493e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.756e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.773e-01
## df
## (Intercept) 8.860e+03
## timefactor2 1.658e+04
## timefactor3 1.665e+04
## PandemicFU2 data collected before COVID-19 1.504e+04
## Age_sexFemales 65+ 1.432e+04
## Age_sexMales 45-64 1.468e+04
## Age_sexMales 65+ 1.467e+04
## EducationHigh School Diploma 8.443e+03
## EducationLess than High School Diploma 8.467e+03
## EducationSome College 8.417e+03
## EthnicityWhite 8.414e+03
## IncomeLevel>$150k 8.433e+03
## IncomeLevel$100-150k 8.424e+03
## IncomeLevel$20-50k 8.435e+03
## IncomeLevel$50-100k 8.435e+03
## BMI 8.424e+03
## CESD.20.1 8.436e+03
## SmokingStatusFormer Smoker 8.435e+03
## SmokingStatusNever Smoked 8.434e+03
## SmokingStatusOccasional Smoker 8.422e+03
## RelationshipstatusMarried 8.450e+03
## RelationshipstatusSeparated 8.457e+03
## RelationshipstatusSingle 8.442e+03
## RelationshipstatusWidowed 8.437e+03
## LivingstatusAssisted Living 8.437e+03
## LivingstatusHouse 8.446e+03
## LivingstatusOther 8.373e+03
## AnxietyYes 8.432e+03
## MoodDisordYes 8.436e+03
## Chronicconditions 8.425e+03
## PASE_TOTALbaseline 8.426e+03
## timefactor2:PandemicFU2 data collected before COVID-19 1.657e+04
## timefactor3:PandemicFU2 data collected before COVID-19 1.662e+04
## timefactor2:Age_sexFemales 65+ 1.656e+04
## timefactor3:Age_sexFemales 65+ 1.665e+04
## timefactor2:Age_sexMales 45-64 1.658e+04
## timefactor3:Age_sexMales 45-64 1.664e+04
## timefactor2:Age_sexMales 65+ 1.656e+04
## timefactor3:Age_sexMales 65+ 1.666e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.504e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.507e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.507e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.656e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.662e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.657e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.660e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.657e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.662e+04
## t value
## (Intercept) 30.019
## timefactor2 -0.722
## timefactor3 3.050
## PandemicFU2 data collected before COVID-19 2.309
## Age_sexFemales 65+ 1.596
## Age_sexMales 45-64 -0.716
## Age_sexMales 65+ -0.870
## EducationHigh School Diploma 3.558
## EducationLess than High School Diploma 3.606
## EducationSome College 2.388
## EthnicityWhite 7.762
## IncomeLevel>$150k 3.875
## IncomeLevel$100-150k 4.115
## IncomeLevel$20-50k 1.052
## IncomeLevel$50-100k 3.733
## BMI -2.942
## CESD.20.1 -5.291
## SmokingStatusFormer Smoker 1.899
## SmokingStatusNever Smoked 1.855
## SmokingStatusOccasional Smoker 1.235
## RelationshipstatusMarried -1.845
## RelationshipstatusSeparated -0.854
## RelationshipstatusSingle -0.673
## RelationshipstatusWidowed -1.403
## LivingstatusAssisted Living -0.284
## LivingstatusHouse 3.721
## LivingstatusOther 0.367
## AnxietyYes -0.588
## MoodDisordYes 2.251
## Chronicconditions -0.793
## PASE_TOTALbaseline 1.940
## timefactor2:PandemicFU2 data collected before COVID-19 1.245
## timefactor3:PandemicFU2 data collected before COVID-19 0.239
## timefactor2:Age_sexFemales 65+ -2.561
## timefactor3:Age_sexFemales 65+ -5.274
## timefactor2:Age_sexMales 45-64 -0.275
## timefactor3:Age_sexMales 45-64 -2.032
## timefactor2:Age_sexMales 65+ -0.917
## timefactor3:Age_sexMales 65+ -4.808
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -0.463
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.037
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -0.588
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.380
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.028
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.161
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.119
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -0.923
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.173
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 0.470590
## timefactor3 0.002289
## PandemicFU2 data collected before COVID-19 0.020977
## Age_sexFemales 65+ 0.110605
## Age_sexMales 45-64 0.473713
## Age_sexMales 65+ 0.384481
## EducationHigh School Diploma 0.000376
## EducationLess than High School Diploma 0.000313
## EducationSome College 0.016984
## EthnicityWhite 9.34e-15
## IncomeLevel>$150k 0.000107
## IncomeLevel$100-150k 3.91e-05
## IncomeLevel$20-50k 0.292906
## IncomeLevel$50-100k 0.000190
## BMI 0.003265
## CESD.20.1 1.25e-07
## SmokingStatusFormer Smoker 0.057580
## SmokingStatusNever Smoked 0.063618
## SmokingStatusOccasional Smoker 0.216977
## RelationshipstatusMarried 0.065078
## RelationshipstatusSeparated 0.392982
## RelationshipstatusSingle 0.501279
## RelationshipstatusWidowed 0.160664
## LivingstatusAssisted Living 0.776684
## LivingstatusHouse 0.000200
## LivingstatusOther 0.713840
## AnxietyYes 0.556517
## MoodDisordYes 0.024421
## Chronicconditions 0.427715
## PASE_TOTALbaseline 0.052405
## timefactor2:PandemicFU2 data collected before COVID-19 0.213050
## timefactor3:PandemicFU2 data collected before COVID-19 0.810898
## timefactor2:Age_sexFemales 65+ 0.010442
## timefactor3:Age_sexFemales 65+ 1.35e-07
## timefactor2:Age_sexMales 45-64 0.783450
## timefactor3:Age_sexMales 45-64 0.042128
## timefactor2:Age_sexMales 65+ 0.359398
## timefactor3:Age_sexMales 65+ 1.54e-06
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.643540
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.299825
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.556239
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.703939
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.303965
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.245481
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.904935
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.356151
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.240625
##
## (Intercept) ***
## timefactor2
## timefactor3 **
## PandemicFU2 data collected before COVID-19 *
## Age_sexFemales 65+
## Age_sexMales 45-64
## Age_sexMales 65+
## EducationHigh School Diploma ***
## EducationLess than High School Diploma ***
## EducationSome College *
## EthnicityWhite ***
## IncomeLevel>$150k ***
## IncomeLevel$100-150k ***
## IncomeLevel$20-50k
## IncomeLevel$50-100k ***
## BMI **
## CESD.20.1 ***
## SmokingStatusFormer Smoker .
## SmokingStatusNever Smoked .
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried .
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed
## LivingstatusAssisted Living
## LivingstatusHouse ***
## LivingstatusOther
## AnxietyYes
## MoodDisordYes *
## Chronicconditions
## PASE_TOTALbaseline .
## timefactor2:PandemicFU2 data collected before COVID-19
## timefactor3:PandemicFU2 data collected before COVID-19
## timefactor2:Age_sexFemales 65+ *
## timefactor3:Age_sexFemales 65+ ***
## timefactor2:Age_sexMales 45-64
## timefactor3:Age_sexMales 45-64 *
## timefactor2:Age_sexMales 65+
## timefactor3:Age_sexMales 65+ ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Significant differences for females 45-64 and 65+ and significant differences males 65+
lsmeans(modelAnimals_11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.740634 0.1985793 Inf 9.351426 10.129843
## FU2 data collected before COVID-19 10.002053 0.1920666 Inf 9.625610 10.378497
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.681621 0.1988456 Inf 9.291891 10.071351
## FU2 data collected before COVID-19 10.073236 0.1921793 Inf 9.696571 10.449900
##
## timefactor = 3, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.993245 0.1992267 Inf 9.602768 10.383722
## FU2 data collected before COVID-19 10.279876 0.1921899 Inf 9.903191 10.656561
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.996888 0.2188953 Inf 9.567861 10.425915
## FU2 data collected before COVID-19 10.167868 0.2068014 Inf 9.762545 10.573191
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.575133 0.2190351 Inf 9.145832 10.004434
## FU2 data collected before COVID-19 9.944764 0.2069864 Inf 9.539078 10.350450
##
## timefactor = 3, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.490882 0.2200025 Inf 9.059685 9.922079
## FU2 data collected before COVID-19 9.874167 0.2071267 Inf 9.468206 10.280128
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.656228 0.1915284 Inf 9.280839 10.031616
## FU2 data collected before COVID-19 9.751059 0.1976118 Inf 9.363747 10.138372
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.567904 0.1917031 Inf 9.192173 9.943635
## FU2 data collected before COVID-19 9.620233 0.1977969 Inf 9.232558 10.007908
##
## timefactor = 3, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.689977 0.1918400 Inf 9.313978 10.065977
## FU2 data collected before COVID-19 9.792190 0.1976952 Inf 9.404715 10.179666
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.610374 0.2159710 Inf 9.187078 10.033669
## FU2 data collected before COVID-19 9.760046 0.2051867 Inf 9.357887 10.162204
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.427494 0.2161783 Inf 9.003792 9.851195
## FU2 data collected before COVID-19 9.545366 0.2053791 Inf 9.142830 9.947901
##
## timefactor = 3, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.202597 0.2172110 Inf 8.776871 9.628322
## FU2 data collected before COVID-19 9.585514 0.2054154 Inf 9.182907 9.988121
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2614191 0.1132349 Inf -2.309 0.0210
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3916145 0.1138134 Inf -3.441 0.0006
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2866313 0.1145714 Inf -2.502 0.0124
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1709802 0.1595874 Inf -1.071 0.2840
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3696308 0.1600727 Inf -2.309 0.0209
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3832849 0.1616409 Inf -2.371 0.0177
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0948318 0.1144394 Inf -0.829 0.4073
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0523290 0.1150952 Inf -0.455 0.6494
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1022127 0.1151366 Inf -0.888 0.3747
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1496718 0.1526477 Inf -0.981 0.3268
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1178723 0.1532604 Inf -0.769 0.4418
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3829171 0.1546809 Inf -2.476 0.0133
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelAnimals_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2614191 0.1132349 Inf -0.4833555 -0.03948266
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3916145 0.1138134 Inf -0.6146846 -0.16854440
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2866313 0.1145714 Inf -0.5111871 -0.06207549
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1709802 0.1595874 Inf -0.4837658 0.14180531
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3696308 0.1600727 Inf -0.6833674 -0.05589413
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3832849 0.1616409 Inf -0.7000952 -0.06647459
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0948318 0.1144394 Inf -0.3191289 0.12946523
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0523290 0.1150952 Inf -0.2779115 0.17325347
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1022127 0.1151366 Inf -0.3278762 0.12345080
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1496718 0.1526477 Inf -0.4488558 0.14951221
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1178723 0.1532604 Inf -0.4182572 0.18251268
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3829171 0.1546809 Inf -0.6860861 -0.07974808
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
Animals_lsmeans_11 <- summary(lsmeans(modelAnimals_11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 25010' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 25010)' or larger];
## but be warned that this may result in large computation time and memory use.
Animals_lsmeans_11$Time<-NA
Animals_lsmeans_11$Time[Animals_lsmeans_11$timefactor==1]<-"Baseline"
Animals_lsmeans_11$Time[Animals_lsmeans_11$timefactor==2]<-"Follow-up 1"
Animals_lsmeans_11$Time[Animals_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(Animals_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "Animal Fluency (words)", title = "Animal Fluency Score from Baseline to FU2 by Pandemic status") +
theme_bw()
All models use normalized cognitive scores. Each model is adjusted for education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, baseline PASE score, and baseline cognitive performance
Age and Sex grouping
Tracking.data_long_2$Age_sex<-NA
Tracking.data_long_2$Age_sex[Tracking.data_long_2$Age<=64 & Tracking.data_long_2$Sex == "M"]<-"Males 45-64"
Tracking.data_long_2$Age_sex[Tracking.data_long_2$Age<=64 & Tracking.data_long_2$Sex == "F"]<-"Females 45-64"
Tracking.data_long_2$Age_sex[Tracking.data_long_2$Age>64 & Tracking.data_long_2$Sex == "M"]<-"Males 65+"
Tracking.data_long_2$Age_sex[Tracking.data_long_2$Age>64 & Tracking.data_long_2$Sex == "F"]<-"Females 65+"
modelRVLT_imm_adj11<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelRVLT_imm_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Immediate_Normed ~ timefactor * Pandemic * Age_sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
## (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 85600.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6937 -0.5662 -0.0379 0.5265 3.8799
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.106 2.026
## Residual 7.332 2.708
## Number of obs: 16420, groups: ID, 8448
##
## Fixed effects:
## Estimate
## (Intercept) 6.598e+00
## timefactor2 4.080e-01
## PandemicFU2 data collected before COVID-19 1.074e-01
## Age_sexFemales 65+ -8.511e-02
## Age_sexMales 45-64 -7.229e-01
## Age_sexMales 65+ -7.033e-01
## EducationHigh School Diploma 2.655e-01
## EducationLess than High School Diploma 4.949e-01
## EducationSome College 2.222e-01
## EthnicityWhite 4.706e-01
## IncomeLevel>$150k 4.902e-01
## IncomeLevel$100-150k 4.279e-01
## IncomeLevel$20-50k 1.359e-01
## IncomeLevel$50-100k 4.270e-01
## BMI -1.495e-02
## CESD.10baseline -2.478e-02
## SmokingStatusFormer Smoker 1.541e-01
## SmokingStatusNever Smoked 2.666e-01
## SmokingStatusOccasional Smoker -2.947e-02
## RelationshipstatusMarried 3.205e-01
## RelationshipstatusSeparated 2.465e-01
## RelationshipstatusSingle 2.052e-01
## RelationshipstatusWidowed -2.142e-02
## LivingstatusAssisted Living -8.691e-01
## LivingstatusHouse 4.813e-02
## LivingstatusOther -3.586e-01
## AnxietyYes 8.277e-02
## MoodDisordYes -1.564e-01
## Chronicconditions -2.961e-02
## PASE_TOTALbaseline 2.632e-03
## RVLT_Immediate_Normedbaseline 3.501e-01
## timefactor2:PandemicFU2 data collected before COVID-19 -1.361e-01
## timefactor2:Age_sexFemales 65+ -4.960e-01
## timefactor2:Age_sexMales 45-64 9.980e-02
## timefactor2:Age_sexMales 65+ -4.202e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.003e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -4.939e-03
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 8.141e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 7.797e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 9.555e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3.542e-01
## Std. Error
## (Intercept) 3.435e-01
## timefactor2 1.191e-01
## PandemicFU2 data collected before COVID-19 1.324e-01
## Age_sexFemales 65+ 1.874e-01
## Age_sexMales 45-64 1.378e-01
## Age_sexMales 65+ 1.749e-01
## EducationHigh School Diploma 9.709e-02
## EducationLess than High School Diploma 1.376e-01
## EducationSome College 1.206e-01
## EthnicityWhite 1.923e-01
## IncomeLevel>$150k 1.805e-01
## IncomeLevel$100-150k 1.438e-01
## IncomeLevel$20-50k 9.605e-02
## IncomeLevel$50-100k 1.035e-01
## BMI 6.494e-03
## CESD.10baseline 7.739e-03
## SmokingStatusFormer Smoker 1.321e-01
## SmokingStatusNever Smoked 1.376e-01
## SmokingStatusOccasional Smoker 2.601e-01
## RelationshipstatusMarried 1.129e-01
## RelationshipstatusSeparated 2.222e-01
## RelationshipstatusSingle 1.522e-01
## RelationshipstatusWidowed 1.536e-01
## LivingstatusAssisted Living 4.571e-01
## LivingstatusHouse 1.016e-01
## LivingstatusOther 3.779e-01
## AnxietyYes 1.353e-01
## MoodDisordYes 9.832e-02
## Chronicconditions 1.572e-02
## PASE_TOTALbaseline 4.477e-04
## RVLT_Immediate_Normedbaseline 8.154e-03
## timefactor2:PandemicFU2 data collected before COVID-19 1.514e-01
## timefactor2:Age_sexFemales 65+ 2.067e-01
## timefactor2:Age_sexMales 45-64 1.547e-01
## timefactor2:Age_sexMales 65+ 1.976e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.286e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.879e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.222e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.619e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.145e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.551e-01
## df
## (Intercept) 8.822e+03
## timefactor2 8.318e+03
## PandemicFU2 data collected before COVID-19 1.461e+04
## Age_sexFemales 65+ 1.414e+04
## Age_sexMales 45-64 1.440e+04
## Age_sexMales 65+ 1.439e+04
## EducationHigh School Diploma 8.371e+03
## EducationLess than High School Diploma 8.502e+03
## EducationSome College 8.293e+03
## EthnicityWhite 8.288e+03
## IncomeLevel>$150k 8.368e+03
## IncomeLevel$100-150k 8.319e+03
## IncomeLevel$20-50k 8.376e+03
## IncomeLevel$50-100k 8.372e+03
## BMI 8.343e+03
## CESD.10baseline 8.368e+03
## SmokingStatusFormer Smoker 8.390e+03
## SmokingStatusNever Smoked 8.390e+03
## SmokingStatusOccasional Smoker 8.262e+03
## RelationshipstatusMarried 8.390e+03
## RelationshipstatusSeparated 8.457e+03
## RelationshipstatusSingle 8.379e+03
## RelationshipstatusWidowed 8.396e+03
## LivingstatusAssisted Living 8.431e+03
## LivingstatusHouse 8.370e+03
## LivingstatusOther 8.285e+03
## AnxietyYes 8.363e+03
## MoodDisordYes 8.364e+03
## Chronicconditions 8.367e+03
## PASE_TOTALbaseline 8.355e+03
## RVLT_Immediate_Normedbaseline 8.383e+03
## timefactor2:PandemicFU2 data collected before COVID-19 8.244e+03
## timefactor2:Age_sexFemales 65+ 8.312e+03
## timefactor2:Age_sexMales 45-64 8.271e+03
## timefactor2:Age_sexMales 65+ 8.337e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.463e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.465e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.466e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 8.270e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 8.205e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 8.270e+03
## t value
## (Intercept) 19.206
## timefactor2 3.426
## PandemicFU2 data collected before COVID-19 0.811
## Age_sexFemales 65+ -0.454
## Age_sexMales 45-64 -5.247
## Age_sexMales 65+ -4.021
## EducationHigh School Diploma 2.735
## EducationLess than High School Diploma 3.597
## EducationSome College 1.842
## EthnicityWhite 2.448
## IncomeLevel>$150k 2.716
## IncomeLevel$100-150k 2.975
## IncomeLevel$20-50k 1.415
## IncomeLevel$50-100k 4.126
## BMI -2.303
## CESD.10baseline -3.202
## SmokingStatusFormer Smoker 1.166
## SmokingStatusNever Smoked 1.937
## SmokingStatusOccasional Smoker -0.113
## RelationshipstatusMarried 2.838
## RelationshipstatusSeparated 1.109
## RelationshipstatusSingle 1.348
## RelationshipstatusWidowed -0.139
## LivingstatusAssisted Living -1.902
## LivingstatusHouse 0.474
## LivingstatusOther -0.949
## AnxietyYes 0.612
## MoodDisordYes -1.591
## Chronicconditions -1.883
## PASE_TOTALbaseline 5.880
## RVLT_Immediate_Normedbaseline 42.933
## timefactor2:PandemicFU2 data collected before COVID-19 -0.899
## timefactor2:Age_sexFemales 65+ -2.400
## timefactor2:Age_sexMales 45-64 0.645
## timefactor2:Age_sexMales 65+ -2.127
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.439
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.026
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.366
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.298
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.445
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.389
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 0.000617
## PandemicFU2 data collected before COVID-19 0.417244
## Age_sexFemales 65+ 0.649740
## Age_sexMales 45-64 1.56e-07
## Age_sexMales 65+ 5.83e-05
## EducationHigh School Diploma 0.006256
## EducationLess than High School Diploma 0.000324
## EducationSome College 0.065453
## EthnicityWhite 0.014399
## IncomeLevel>$150k 0.006619
## IncomeLevel$100-150k 0.002940
## IncomeLevel$20-50k 0.157006
## IncomeLevel$50-100k 3.73e-05
## BMI 0.021330
## CESD.10baseline 0.001371
## SmokingStatusFormer Smoker 0.243499
## SmokingStatusNever Smoked 0.052758
## SmokingStatusOccasional Smoker 0.909795
## RelationshipstatusMarried 0.004549
## RelationshipstatusSeparated 0.267331
## RelationshipstatusSingle 0.177595
## RelationshipstatusWidowed 0.889120
## LivingstatusAssisted Living 0.057266
## LivingstatusHouse 0.635792
## LivingstatusOther 0.342630
## AnxietyYes 0.540833
## MoodDisordYes 0.111633
## Chronicconditions 0.059676
## PASE_TOTALbaseline 4.26e-09
## RVLT_Immediate_Normedbaseline < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19 0.368552
## timefactor2:Age_sexFemales 65+ 0.016419
## timefactor2:Age_sexMales 45-64 0.518835
## timefactor2:Age_sexMales 65+ 0.033486
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.660764
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.979026
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.714099
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.765955
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.655980
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.164935
##
## (Intercept) ***
## timefactor2 ***
## PandemicFU2 data collected before COVID-19
## Age_sexFemales 65+
## Age_sexMales 45-64 ***
## Age_sexMales 65+ ***
## EducationHigh School Diploma **
## EducationLess than High School Diploma ***
## EducationSome College .
## EthnicityWhite *
## IncomeLevel>$150k **
## IncomeLevel$100-150k **
## IncomeLevel$20-50k
## IncomeLevel$50-100k ***
## BMI *
## CESD.10baseline **
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked .
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried **
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed
## LivingstatusAssisted Living .
## LivingstatusHouse
## LivingstatusOther
## AnxietyYes
## MoodDisordYes
## Chronicconditions .
## PASE_TOTALbaseline ***
## RVLT_Immediate_Normedbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19
## timefactor2:Age_sexFemales 65+ *
## timefactor2:Age_sexMales 45-64
## timefactor2:Age_sexMales 65+ *
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Significantly lower RVLT immediate for males 65+
lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.80715 0.2268978 Inf 10.362437 11.25186
## FU2 data collected before COVID-19 10.91454 0.2188850 Inf 10.485537 11.34355
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.21513 0.2277087 Inf 10.768825 11.66143
## FU2 data collected before COVID-19 11.18638 0.2188665 Inf 10.757406 11.61535
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.72204 0.2506661 Inf 10.230742 11.21334
## FU2 data collected before COVID-19 10.92976 0.2367831 Inf 10.465678 11.39385
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.63397 0.2523695 Inf 10.139338 11.12861
## FU2 data collected before COVID-19 10.78353 0.2367328 Inf 10.319539 11.24751
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.08427 0.2186070 Inf 9.655806 10.51273
## FU2 data collected before COVID-19 10.18672 0.2257134 Inf 9.744333 10.62911
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.59205 0.2188164 Inf 10.163174 11.02092
## FU2 data collected before COVID-19 10.65391 0.2254293 Inf 10.212073 11.09574
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.10386 0.2473325 Inf 9.619100 10.58863
## FU2 data collected before COVID-19 10.29267 0.2347117 Inf 9.832645 10.75270
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.09165 0.2492150 Inf 9.603193 10.58010
## FU2 data collected before COVID-19 10.49855 0.2345335 Inf 10.038877 10.95823
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1073953 0.1323848 Inf -0.811 0.4172
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.0287488 0.1339334 Inf 0.215 0.8300
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2077263 0.1867247 Inf -1.112 0.2659
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1495543 0.1892579 Inf -0.790 0.4294
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1024559 0.1340684 Inf -0.764 0.4447
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0618604 0.1340137 Inf -0.462 0.6444
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1888088 0.1787487 Inf -1.056 0.2908
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4069084 0.1809425 Inf -2.249 0.0245
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1073953 0.1323848 Inf -0.3668647 0.1520741
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.0287488 0.1339334 Inf -0.2337558 0.2912535
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2077263 0.1867247 Inf -0.5737000 0.1582474
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1495543 0.1892579 Inf -0.5204929 0.2213844
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1024559 0.1340684 Inf -0.3652251 0.1603133
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0618604 0.1340137 Inf -0.3245225 0.2008016
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1888088 0.1787487 Inf -0.5391498 0.1615323
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4069084 0.1809425 Inf -0.7615492 -0.0522677
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTimmediate_lsmeans_adj11 <- summary(lsmeans(modelRVLT_imm_adj11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16420)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTimmediate_lsmeans_adj11$Time<-NA
RVLTimmediate_lsmeans_adj11$Time[RVLTimmediate_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj11$Time[RVLTimmediate_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
modelRVLT_del_adj11<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelRVLT_del_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic * Age_sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
## (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 84238.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9037 -0.5514 -0.0365 0.5079 3.9605
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.124 2.031
## Residual 6.928 2.632
## Number of obs: 16282, groups: ID, 8435
##
## Fixed effects:
## Estimate
## (Intercept) 6.088e+00
## timefactor2 8.002e-01
## PandemicFU2 data collected before COVID-19 2.208e-01
## Age_sexFemales 65+ 1.272e-01
## Age_sexMales 45-64 -5.228e-01
## Age_sexMales 65+ -4.201e-01
## EducationHigh School Diploma 2.951e-01
## EducationLess than High School Diploma 3.835e-01
## EducationSome College 3.010e-01
## EthnicityWhite 7.020e-01
## IncomeLevel>$150k 4.284e-01
## IncomeLevel$100-150k 3.492e-01
## IncomeLevel$20-50k 1.516e-01
## IncomeLevel$50-100k 4.574e-01
## BMI -1.686e-02
## CESD.10baseline -2.066e-02
## SmokingStatusFormer Smoker 7.169e-02
## SmokingStatusNever Smoked 2.521e-01
## SmokingStatusOccasional Smoker 6.303e-02
## RelationshipstatusMarried 9.608e-02
## RelationshipstatusSeparated -4.161e-02
## RelationshipstatusSingle 4.656e-02
## RelationshipstatusWidowed -1.809e-01
## LivingstatusAssisted Living -1.073e+00
## LivingstatusHouse 1.069e-01
## LivingstatusOther -2.054e-01
## AnxietyYes 1.123e-01
## MoodDisordYes -1.620e-01
## Chronicconditions -3.713e-02
## PASE_TOTALbaseline 2.565e-03
## RVLT_Delayed_Normedbaseline 3.914e-01
## timefactor2:PandemicFU2 data collected before COVID-19 -3.946e-01
## timefactor2:Age_sexFemales 65+ -6.193e-01
## timefactor2:Age_sexMales 45-64 -8.342e-02
## timefactor2:Age_sexMales 65+ -5.891e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.545e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -3.201e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 6.066e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.918e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 6.574e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 4.066e-01
## Std. Error
## (Intercept) 3.428e-01
## timefactor2 1.160e-01
## PandemicFU2 data collected before COVID-19 1.304e-01
## Age_sexFemales 65+ 1.848e-01
## Age_sexMales 45-64 1.360e-01
## Age_sexMales 65+ 1.732e-01
## EducationHigh School Diploma 9.623e-02
## EducationLess than High School Diploma 1.368e-01
## EducationSome College 1.194e-01
## EthnicityWhite 1.916e-01
## IncomeLevel>$150k 1.785e-01
## IncomeLevel$100-150k 1.425e-01
## IncomeLevel$20-50k 9.519e-02
## IncomeLevel$50-100k 1.025e-01
## BMI 6.430e-03
## CESD.10baseline 7.676e-03
## SmokingStatusFormer Smoker 1.308e-01
## SmokingStatusNever Smoked 1.362e-01
## SmokingStatusOccasional Smoker 2.579e-01
## RelationshipstatusMarried 1.119e-01
## RelationshipstatusSeparated 2.203e-01
## RelationshipstatusSingle 1.507e-01
## RelationshipstatusWidowed 1.522e-01
## LivingstatusAssisted Living 4.533e-01
## LivingstatusHouse 1.008e-01
## LivingstatusOther 3.752e-01
## AnxietyYes 1.341e-01
## MoodDisordYes 9.734e-02
## Chronicconditions 1.558e-02
## PASE_TOTALbaseline 4.437e-04
## RVLT_Delayed_Normedbaseline 8.294e-03
## timefactor2:PandemicFU2 data collected before COVID-19 1.475e-01
## timefactor2:Age_sexFemales 65+ 2.026e-01
## timefactor2:Age_sexMales 45-64 1.510e-01
## timefactor2:Age_sexMales 65+ 1.939e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.252e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.852e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.199e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.563e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.093e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.502e-01
## df
## (Intercept) 8.838e+03
## timefactor2 8.209e+03
## PandemicFU2 data collected before COVID-19 1.440e+04
## Age_sexFemales 65+ 1.393e+04
## Age_sexMales 45-64 1.422e+04
## Age_sexMales 65+ 1.425e+04
## EducationHigh School Diploma 8.354e+03
## EducationLess than High School Diploma 8.546e+03
## EducationSome College 8.272e+03
## EthnicityWhite 8.389e+03
## IncomeLevel>$150k 8.342e+03
## IncomeLevel$100-150k 8.313e+03
## IncomeLevel$20-50k 8.374e+03
## IncomeLevel$50-100k 8.353e+03
## BMI 8.315e+03
## CESD.10baseline 8.379e+03
## SmokingStatusFormer Smoker 8.365e+03
## SmokingStatusNever Smoked 8.364e+03
## SmokingStatusOccasional Smoker 8.283e+03
## RelationshipstatusMarried 8.344e+03
## RelationshipstatusSeparated 8.467e+03
## RelationshipstatusSingle 8.332e+03
## RelationshipstatusWidowed 8.362e+03
## LivingstatusAssisted Living 8.459e+03
## LivingstatusHouse 8.371e+03
## LivingstatusOther 8.336e+03
## AnxietyYes 8.332e+03
## MoodDisordYes 8.338e+03
## Chronicconditions 8.349e+03
## PASE_TOTALbaseline 8.350e+03
## RVLT_Delayed_Normedbaseline 8.356e+03
## timefactor2:PandemicFU2 data collected before COVID-19 8.139e+03
## timefactor2:Age_sexFemales 65+ 8.266e+03
## timefactor2:Age_sexMales 45-64 8.184e+03
## timefactor2:Age_sexMales 65+ 8.313e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.442e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.445e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.450e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 8.209e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 8.119e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 8.238e+03
## t value
## (Intercept) 17.760
## timefactor2 6.896
## PandemicFU2 data collected before COVID-19 1.693
## Age_sexFemales 65+ 0.689
## Age_sexMales 45-64 -3.845
## Age_sexMales 65+ -2.426
## EducationHigh School Diploma 3.067
## EducationLess than High School Diploma 2.804
## EducationSome College 2.522
## EthnicityWhite 3.664
## IncomeLevel>$150k 2.399
## IncomeLevel$100-150k 2.450
## IncomeLevel$20-50k 1.592
## IncomeLevel$50-100k 4.462
## BMI -2.622
## CESD.10baseline -2.692
## SmokingStatusFormer Smoker 0.548
## SmokingStatusNever Smoked 1.852
## SmokingStatusOccasional Smoker 0.244
## RelationshipstatusMarried 0.858
## RelationshipstatusSeparated -0.189
## RelationshipstatusSingle 0.309
## RelationshipstatusWidowed -1.189
## LivingstatusAssisted Living -2.367
## LivingstatusHouse 1.060
## LivingstatusOther -0.548
## AnxietyYes 0.837
## MoodDisordYes -1.664
## Chronicconditions -2.383
## PASE_TOTALbaseline 5.782
## RVLT_Delayed_Normedbaseline 47.190
## timefactor2:PandemicFU2 data collected before COVID-19 -2.675
## timefactor2:Age_sexFemales 65+ -3.057
## timefactor2:Age_sexMales 45-64 -0.553
## timefactor2:Age_sexMales 65+ -3.038
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.686
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.173
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.276
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.138
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.314
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.625
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 5.75e-12
## PandemicFU2 data collected before COVID-19 0.090473
## Age_sexFemales 65+ 0.491093
## Age_sexMales 45-64 0.000121
## Age_sexMales 65+ 0.015285
## EducationHigh School Diploma 0.002172
## EducationLess than High School Diploma 0.005060
## EducationSome College 0.011704
## EthnicityWhite 0.000250
## IncomeLevel>$150k 0.016449
## IncomeLevel$100-150k 0.014313
## IncomeLevel$20-50k 0.111404
## IncomeLevel$50-100k 8.23e-06
## BMI 0.008747
## CESD.10baseline 0.007119
## SmokingStatusFormer Smoker 0.583534
## SmokingStatusNever Smoked 0.064115
## SmokingStatusOccasional Smoker 0.806911
## RelationshipstatusMarried 0.390730
## RelationshipstatusSeparated 0.850198
## RelationshipstatusSingle 0.757453
## RelationshipstatusWidowed 0.234578
## LivingstatusAssisted Living 0.017957
## LivingstatusHouse 0.288988
## LivingstatusOther 0.584020
## AnxietyYes 0.402442
## MoodDisordYes 0.096149
## Chronicconditions 0.017197
## PASE_TOTALbaseline 7.66e-09
## RVLT_Delayed_Normedbaseline < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19 0.007484
## timefactor2:Age_sexFemales 65+ 0.002241
## timefactor2:Age_sexMales 45-64 0.580570
## timefactor2:Age_sexMales 65+ 0.002391
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.492649
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.862807
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.782699
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.254977
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.753427
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.104196
##
## (Intercept) ***
## timefactor2 ***
## PandemicFU2 data collected before COVID-19 .
## Age_sexFemales 65+
## Age_sexMales 45-64 ***
## Age_sexMales 65+ *
## EducationHigh School Diploma **
## EducationLess than High School Diploma **
## EducationSome College *
## EthnicityWhite ***
## IncomeLevel>$150k *
## IncomeLevel$100-150k *
## IncomeLevel$20-50k
## IncomeLevel$50-100k ***
## BMI **
## CESD.10baseline **
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked .
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed
## LivingstatusAssisted Living *
## LivingstatusHouse
## LivingstatusOther
## AnxietyYes
## MoodDisordYes .
## Chronicconditions *
## PASE_TOTALbaseline ***
## RVLT_Delayed_Normedbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19 **
## timefactor2:Age_sexFemales 65+ **
## timefactor2:Age_sexMales 45-64
## timefactor2:Age_sexMales 65+ **
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
No significant differences
lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.71833 0.2249975 Inf 10.277338 11.15931
## FU2 data collected before COVID-19 10.93916 0.2170342 Inf 10.513781 11.36454
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.51857 0.2255048 Inf 11.076589 11.96055
## FU2 data collected before COVID-19 11.34477 0.2170287 Inf 10.919397 11.77013
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.84558 0.2481783 Inf 10.359154 11.33199
## FU2 data collected before COVID-19 11.22095 0.2346880 Inf 10.760971 11.68093
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.02656 0.2508985 Inf 10.534805 11.51831
## FU2 data collected before COVID-19 11.29904 0.2347217 Inf 10.838998 11.75909
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.19548 0.2167819 Inf 9.770600 10.62037
## FU2 data collected before COVID-19 10.38432 0.2235456 Inf 9.946174 10.82246
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.91231 0.2168276 Inf 10.487336 11.33728
## FU2 data collected before COVID-19 10.77224 0.2235154 Inf 10.334154 11.21032
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.29818 0.2454416 Inf 9.817120 10.77923
## FU2 data collected before COVID-19 10.57967 0.2330089 Inf 10.122985 11.03636
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.50936 0.2472205 Inf 10.024815 10.99390
## FU2 data collected before COVID-19 10.80277 0.2325377 Inf 10.347005 11.25854
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2208354 0.1304385 Inf -1.693 0.0905
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.1738044 0.1317497 Inf 1.319 0.1871
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3753766 0.1839559 Inf -2.041 0.0413
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2724873 0.1875240 Inf -1.453 0.1462
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1888302 0.1322031 Inf -1.428 0.1532
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.1400740 0.1320982 Inf 1.060 0.2890
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2814974 0.1773064 Inf -1.588 0.1124
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2934125 0.1790454 Inf -1.639 0.1013
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2208354 0.1304385 Inf -0.4764901 0.0348194
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.1738044 0.1317497 Inf -0.0844203 0.4320290
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3753766 0.1839559 Inf -0.7359235 -0.0148297
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2724873 0.1875240 Inf -0.6400276 0.0950530
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1888302 0.1322031 Inf -0.4479436 0.0702832
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.1400740 0.1320982 Inf -0.1188337 0.3989817
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2814974 0.1773064 Inf -0.6290115 0.0660168
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2934125 0.1790454 Inf -0.6443350 0.0575100
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTdelayed_lsmeans_adj11 <- summary(lsmeans(modelRVLT_del_adj11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16282' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16282)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTdelayed_lsmeans_adj11$Time<-NA
RVLTdelayed_lsmeans_adj11$Time[RVLTdelayed_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj11$Time[RVLTdelayed_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
modelMAT_adj11<- lmer(MAT_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelMAT_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## MAT_Normed ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +
## IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
## Livingstatus + Anxiety + MoodDisord + Chronicconditions +
## PASE_TOTALbaseline + MAT_Normedbaseline + (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 79003.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4602 -0.5125 -0.0493 0.3954 4.6769
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.614 1.617
## Residual 7.688 2.773
## Number of obs: 15353, groups: ID, 8344
##
## Fixed effects:
## Estimate
## (Intercept) 8.218e+00
## timefactor2 -3.248e+00
## PandemicFU2 data collected before COVID-19 -1.501e+00
## Age_sexFemales 65+ -7.047e-01
## Age_sexMales 45-64 -3.251e+00
## Age_sexMales 65+ -3.317e+00
## EducationHigh School Diploma -1.764e-02
## EducationLess than High School Diploma -8.423e-02
## EducationSome College -1.048e-01
## EthnicityWhite 8.453e-01
## IncomeLevel>$150k 2.309e-01
## IncomeLevel$100-150k 2.733e-01
## IncomeLevel$20-50k 1.378e-01
## IncomeLevel$50-100k 1.750e-01
## BMI -1.162e-02
## CESD.10baseline -1.832e-02
## SmokingStatusFormer Smoker 8.956e-02
## SmokingStatusNever Smoked 3.153e-02
## SmokingStatusOccasional Smoker 1.909e-01
## RelationshipstatusMarried -5.330e-04
## RelationshipstatusSeparated -9.028e-02
## RelationshipstatusSingle 5.157e-01
## RelationshipstatusWidowed -1.177e-01
## LivingstatusAssisted Living -3.390e-01
## LivingstatusHouse -2.149e-01
## LivingstatusOther -4.006e-01
## AnxietyYes 5.000e-02
## MoodDisordYes 1.269e-01
## Chronicconditions -3.368e-02
## PASE_TOTALbaseline -3.947e-04
## MAT_Normedbaseline 4.483e-01
## timefactor2:PandemicFU2 data collected before COVID-19 1.647e+00
## timefactor2:Age_sexFemales 65+ 4.769e-01
## timefactor2:Age_sexMales 45-64 3.291e+00
## timefactor2:Age_sexMales 65+ 2.887e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 3.811e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.624e+00
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.810e+00
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.312e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.765e+00
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.593e+00
## Std. Error
## (Intercept) 3.272e-01
## timefactor2 1.248e-01
## PandemicFU2 data collected before COVID-19 1.285e-01
## Age_sexFemales 65+ 1.859e-01
## Age_sexMales 45-64 1.342e-01
## Age_sexMales 65+ 1.738e-01
## EducationHigh School Diploma 9.111e-02
## EducationLess than High School Diploma 1.311e-01
## EducationSome College 1.123e-01
## EthnicityWhite 1.823e-01
## IncomeLevel>$150k 1.690e-01
## IncomeLevel$100-150k 1.348e-01
## IncomeLevel$20-50k 9.041e-02
## IncomeLevel$50-100k 9.739e-02
## BMI 6.063e-03
## CESD.10baseline 7.255e-03
## SmokingStatusFormer Smoker 1.234e-01
## SmokingStatusNever Smoked 1.285e-01
## SmokingStatusOccasional Smoker 2.434e-01
## RelationshipstatusMarried 1.060e-01
## RelationshipstatusSeparated 2.078e-01
## RelationshipstatusSingle 1.427e-01
## RelationshipstatusWidowed 1.448e-01
## LivingstatusAssisted Living 4.298e-01
## LivingstatusHouse 9.567e-02
## LivingstatusOther 3.570e-01
## AnxietyYes 1.264e-01
## MoodDisordYes 9.176e-02
## Chronicconditions 1.480e-02
## PASE_TOTALbaseline 4.183e-04
## MAT_Normedbaseline 8.505e-03
## timefactor2:PandemicFU2 data collected before COVID-19 1.587e-01
## timefactor2:Age_sexFemales 65+ 2.238e-01
## timefactor2:Age_sexMales 45-64 1.628e-01
## timefactor2:Age_sexMales 65+ 2.144e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.262e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.833e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.201e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.817e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.262e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.755e-01
## df
## (Intercept) 8.740e+03
## timefactor2 7.722e+03
## PandemicFU2 data collected before COVID-19 1.461e+04
## Age_sexFemales 65+ 1.441e+04
## Age_sexMales 45-64 1.446e+04
## Age_sexMales 65+ 1.451e+04
## EducationHigh School Diploma 8.100e+03
## EducationLess than High School Diploma 8.347e+03
## EducationSome College 7.992e+03
## EthnicityWhite 8.225e+03
## IncomeLevel>$150k 8.088e+03
## IncomeLevel$100-150k 8.064e+03
## IncomeLevel$20-50k 8.162e+03
## IncomeLevel$50-100k 8.115e+03
## BMI 8.003e+03
## CESD.10baseline 8.063e+03
## SmokingStatusFormer Smoker 8.051e+03
## SmokingStatusNever Smoked 8.052e+03
## SmokingStatusOccasional Smoker 7.922e+03
## RelationshipstatusMarried 8.160e+03
## RelationshipstatusSeparated 8.211e+03
## RelationshipstatusSingle 8.128e+03
## RelationshipstatusWidowed 8.211e+03
## LivingstatusAssisted Living 8.094e+03
## LivingstatusHouse 8.172e+03
## LivingstatusOther 8.166e+03
## AnxietyYes 8.068e+03
## MoodDisordYes 8.043e+03
## Chronicconditions 8.159e+03
## PASE_TOTALbaseline 8.059e+03
## MAT_Normedbaseline 8.157e+03
## timefactor2:PandemicFU2 data collected before COVID-19 7.648e+03
## timefactor2:Age_sexFemales 65+ 8.109e+03
## timefactor2:Age_sexMales 45-64 7.716e+03
## timefactor2:Age_sexMales 65+ 8.061e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.470e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.465e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.469e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 7.987e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 7.685e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 7.969e+03
## t value
## (Intercept) 25.117
## timefactor2 -26.018
## PandemicFU2 data collected before COVID-19 -11.678
## Age_sexFemales 65+ -3.791
## Age_sexMales 45-64 -24.226
## Age_sexMales 65+ -19.084
## EducationHigh School Diploma -0.194
## EducationLess than High School Diploma -0.642
## EducationSome College -0.933
## EthnicityWhite 4.638
## IncomeLevel>$150k 1.367
## IncomeLevel$100-150k 2.028
## IncomeLevel$20-50k 1.524
## IncomeLevel$50-100k 1.797
## BMI -1.916
## CESD.10baseline -2.525
## SmokingStatusFormer Smoker 0.726
## SmokingStatusNever Smoked 0.245
## SmokingStatusOccasional Smoker 0.784
## RelationshipstatusMarried -0.005
## RelationshipstatusSeparated -0.434
## RelationshipstatusSingle 3.615
## RelationshipstatusWidowed -0.813
## LivingstatusAssisted Living -0.789
## LivingstatusHouse -2.246
## LivingstatusOther -1.122
## AnxietyYes 0.396
## MoodDisordYes 1.383
## Chronicconditions -2.275
## PASE_TOTALbaseline -0.944
## MAT_Normedbaseline 52.717
## timefactor2:PandemicFU2 data collected before COVID-19 10.378
## timefactor2:Age_sexFemales 65+ 2.131
## timefactor2:Age_sexMales 45-64 20.216
## timefactor2:Age_sexMales 65+ 13.461
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.169
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 8.863
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 8.222
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.821
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -7.800
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -5.780
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 < 2e-16
## PandemicFU2 data collected before COVID-19 < 2e-16
## Age_sexFemales 65+ 0.000151
## Age_sexMales 45-64 < 2e-16
## Age_sexMales 65+ < 2e-16
## EducationHigh School Diploma 0.846506
## EducationLess than High School Diploma 0.520609
## EducationSome College 0.350710
## EthnicityWhite 3.57e-06
## IncomeLevel>$150k 0.171799
## IncomeLevel$100-150k 0.042625
## IncomeLevel$20-50k 0.127622
## IncomeLevel$50-100k 0.072414
## BMI 0.055374
## CESD.10baseline 0.011573
## SmokingStatusFormer Smoker 0.467980
## SmokingStatusNever Smoked 0.806136
## SmokingStatusOccasional Smoker 0.432873
## RelationshipstatusMarried 0.995989
## RelationshipstatusSeparated 0.663963
## RelationshipstatusSingle 0.000302
## RelationshipstatusWidowed 0.416131
## LivingstatusAssisted Living 0.430224
## LivingstatusHouse 0.024738
## LivingstatusOther 0.261936
## AnxietyYes 0.692295
## MoodDisordYes 0.166645
## Chronicconditions 0.022931
## PASE_TOTALbaseline 0.345432
## MAT_Normedbaseline < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19 < 2e-16
## timefactor2:Age_sexFemales 65+ 0.033135
## timefactor2:Age_sexMales 45-64 < 2e-16
## timefactor2:Age_sexMales 65+ < 2e-16
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.866183
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 < 2e-16
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.411799
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 7.02e-15
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 7.73e-09
##
## (Intercept) ***
## timefactor2 ***
## PandemicFU2 data collected before COVID-19 ***
## Age_sexFemales 65+ ***
## Age_sexMales 45-64 ***
## Age_sexMales 65+ ***
## EducationHigh School Diploma
## EducationLess than High School Diploma
## EducationSome College
## EthnicityWhite ***
## IncomeLevel>$150k
## IncomeLevel$100-150k *
## IncomeLevel$20-50k
## IncomeLevel$50-100k .
## BMI .
## CESD.10baseline *
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle ***
## RelationshipstatusWidowed
## LivingstatusAssisted Living
## LivingstatusHouse *
## LivingstatusOther
## AnxietyYes
## MoodDisordYes
## Chronicconditions *
## PASE_TOTALbaseline
## MAT_Normedbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19 ***
## timefactor2:Age_sexFemales 65+ *
## timefactor2:Age_sexMales 45-64 ***
## timefactor2:Age_sexMales 65+ ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 ***
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ ***
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 ***
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Significantly lower MAT score for males and females 65+
lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 12.710553 0.2153956 Inf 12.288385 13.132720
## FU2 data collected before COVID-19 11.209445 0.2071594 Inf 10.803421 11.615470
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.462678 0.2155738 Inf 9.040162 9.885195
## FU2 data collected before COVID-19 9.608991 0.2068806 Inf 9.203512 10.014469
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 12.005811 0.2419546 Inf 11.531589 12.480034
## FU2 data collected before COVID-19 10.542814 0.2250573 Inf 10.101709 10.983918
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.234855 0.2424101 Inf 8.759740 9.709971
## FU2 data collected before COVID-19 9.650495 0.2254384 Inf 9.208644 10.092346
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.459130 0.2072520 Inf 9.052924 9.865337
## FU2 data collected before COVID-19 9.582490 0.2144732 Inf 9.162131 10.002850
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.502662 0.2068836 Inf 9.097177 9.908146
## FU2 data collected before COVID-19 9.508845 0.2140541 Inf 9.089307 9.928384
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.394004 0.2386336 Inf 8.926291 9.861717
## FU2 data collected before COVID-19 9.702846 0.2241882 Inf 9.263445 10.142247
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.032827 0.2410409 Inf 8.560395 9.505258
## FU2 data collected before COVID-19 9.396384 0.2242718 Inf 8.956819 9.835949
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 1.5011073 0.1285441 Inf 11.678 <.0001
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1463123 0.1292796 Inf -1.132 0.2577
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 1.4629978 0.1863424 Inf 7.851 <.0001
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4156393 0.1886098 Inf -2.204 0.0275
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1233600 0.1311306 Inf -0.941 0.3468
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0061837 0.1303175 Inf -0.047 0.9622
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3088420 0.1789088 Inf -1.726 0.0843
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3635573 0.1827834 Inf -1.989 0.0467
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 1.5011073 0.1285441 Inf 1.2491654 1.7530493
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1463123 0.1292796 Inf -0.3996956 0.1070711
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 1.4629978 0.1863424 Inf 1.0977735 1.8282222
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4156393 0.1886098 Inf -0.7853077 -0.0459710
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1233600 0.1311306 Inf -0.3803714 0.1336513
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0061837 0.1303175 Inf -0.2616012 0.2492339
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3088420 0.1789088 Inf -0.6594967 0.0418127
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3635573 0.1827834 Inf -0.7218062 -0.0053084
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
MAT_lsmeans_adj11 <- summary(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 15353' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 15353)' or larger];
## but be warned that this may result in large computation time and memory use.
MAT_lsmeans_adj11$Time<-NA
MAT_lsmeans_adj11$Time[MAT_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj11$Time[MAT_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
modelAnimals_adj11<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelAnimals_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Animal_Fluency_Normed ~ timefactor * Pandemic * Age_sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
## (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 73825.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8810 -0.5507 -0.0181 0.5304 4.7952
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.383 1.544
## Residual 3.183 1.784
## Number of obs: 16543, groups: ID, 8454
##
## Fixed effects:
## Estimate
## (Intercept) 4.157e+00
## timefactor2 3.121e-01
## PandemicFU2 data collected before COVID-19 2.748e-01
## Age_sexFemales 65+ -1.698e-01
## Age_sexMales 45-64 -4.493e-02
## Age_sexMales 65+ -1.221e-01
## EducationHigh School Diploma 2.332e-01
## EducationLess than High School Diploma 1.416e-01
## EducationSome College 1.652e-01
## EthnicityWhite 4.528e-01
## IncomeLevel>$150k 1.195e-02
## IncomeLevel$100-150k 6.033e-02
## IncomeLevel$20-50k -1.117e-01
## IncomeLevel$50-100k 6.525e-02
## BMI -8.184e-03
## CESD.10baseline -9.592e-03
## SmokingStatusFormer Smoker 1.350e-01
## SmokingStatusNever Smoked 1.667e-01
## SmokingStatusOccasional Smoker -2.123e-02
## RelationshipstatusMarried 4.821e-02
## RelationshipstatusSeparated 1.923e-01
## RelationshipstatusSingle 6.596e-02
## RelationshipstatusWidowed -2.983e-02
## LivingstatusAssisted Living -2.625e-01
## LivingstatusHouse 1.521e-01
## LivingstatusOther -2.770e-01
## AnxietyYes 1.205e-01
## MoodDisordYes 6.869e-02
## Chronicconditions -1.600e-02
## PASE_TOTALbaseline 6.770e-04
## Animal_Fluency_Normedbaseline 5.268e-01
## timefactor2:PandemicFU2 data collected before COVID-19 -1.060e-01
## timefactor2:Age_sexFemales 65+ -3.932e-01
## timefactor2:Age_sexMales 45-64 -1.889e-01
## timefactor2:Age_sexMales 65+ -5.325e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 7.517e-03
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -2.616e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -2.237e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.185e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.534e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3.668e-01
## Std. Error
## (Intercept) 2.471e-01
## timefactor2 7.828e-02
## PandemicFU2 data collected before COVID-19 9.212e-02
## Age_sexFemales 65+ 1.303e-01
## Age_sexMales 45-64 9.596e-02
## Age_sexMales 65+ 1.217e-01
## EducationHigh School Diploma 6.926e-02
## EducationLess than High School Diploma 9.789e-02
## EducationSome College 8.595e-02
## EthnicityWhite 1.374e-01
## IncomeLevel>$150k 1.285e-01
## IncomeLevel$100-150k 1.027e-01
## IncomeLevel$20-50k 6.844e-02
## IncomeLevel$50-100k 7.376e-02
## BMI 4.628e-03
## CESD.10baseline 5.524e-03
## SmokingStatusFormer Smoker 9.417e-02
## SmokingStatusNever Smoked 9.805e-02
## SmokingStatusOccasional Smoker 1.858e-01
## RelationshipstatusMarried 8.058e-02
## RelationshipstatusSeparated 1.583e-01
## RelationshipstatusSingle 1.085e-01
## RelationshipstatusWidowed 1.094e-01
## LivingstatusAssisted Living 3.253e-01
## LivingstatusHouse 7.252e-02
## LivingstatusOther 2.688e-01
## AnxietyYes 9.650e-02
## MoodDisordYes 7.012e-02
## Chronicconditions 1.120e-02
## PASE_TOTALbaseline 3.189e-04
## Animal_Fluency_Normedbaseline 7.167e-03
## timefactor2:PandemicFU2 data collected before COVID-19 9.946e-02
## timefactor2:Age_sexFemales 65+ 1.356e-01
## timefactor2:Age_sexMales 45-64 1.018e-01
## timefactor2:Age_sexMales 65+ 1.296e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.587e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.308e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.543e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.715e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.410e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.672e-01
## df
## (Intercept) 8.772e+03
## timefactor2 8.347e+03
## PandemicFU2 data collected before COVID-19 1.405e+04
## Age_sexFemales 65+ 1.354e+04
## Age_sexMales 45-64 1.384e+04
## Age_sexMales 65+ 1.379e+04
## EducationHigh School Diploma 8.389e+03
## EducationLess than High School Diploma 8.479e+03
## EducationSome College 8.336e+03
## EthnicityWhite 8.308e+03
## IncomeLevel>$150k 8.386e+03
## IncomeLevel$100-150k 8.348e+03
## IncomeLevel$20-50k 8.380e+03
## IncomeLevel$50-100k 8.377e+03
## BMI 8.354e+03
## CESD.10baseline 8.392e+03
## SmokingStatusFormer Smoker 8.395e+03
## SmokingStatusNever Smoked 8.392e+03
## SmokingStatusOccasional Smoker 8.319e+03
## RelationshipstatusMarried 8.415e+03
## RelationshipstatusSeparated 8.450e+03
## RelationshipstatusSingle 8.406e+03
## RelationshipstatusWidowed 8.385e+03
## LivingstatusAssisted Living 8.402e+03
## LivingstatusHouse 8.395e+03
## LivingstatusOther 8.239e+03
## AnxietyYes 8.365e+03
## MoodDisordYes 8.385e+03
## Chronicconditions 8.359e+03
## PASE_TOTALbaseline 8.367e+03
## Animal_Fluency_Normedbaseline 8.361e+03
## timefactor2:PandemicFU2 data collected before COVID-19 8.279e+03
## timefactor2:Age_sexFemales 65+ 8.324e+03
## timefactor2:Age_sexMales 45-64 8.320e+03
## timefactor2:Age_sexMales 65+ 8.350e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.402e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.408e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.406e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 8.276e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 8.251e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 8.285e+03
## t value
## (Intercept) 16.826
## timefactor2 3.988
## PandemicFU2 data collected before COVID-19 2.983
## Age_sexFemales 65+ -1.303
## Age_sexMales 45-64 -0.468
## Age_sexMales 65+ -1.003
## EducationHigh School Diploma 3.367
## EducationLess than High School Diploma 1.447
## EducationSome College 1.922
## EthnicityWhite 3.296
## IncomeLevel>$150k 0.093
## IncomeLevel$100-150k 0.588
## IncomeLevel$20-50k -1.632
## IncomeLevel$50-100k 0.885
## BMI -1.768
## CESD.10baseline -1.736
## SmokingStatusFormer Smoker 1.434
## SmokingStatusNever Smoked 1.700
## SmokingStatusOccasional Smoker -0.114
## RelationshipstatusMarried 0.598
## RelationshipstatusSeparated 1.215
## RelationshipstatusSingle 0.608
## RelationshipstatusWidowed -0.273
## LivingstatusAssisted Living -0.807
## LivingstatusHouse 2.097
## LivingstatusOther -1.031
## AnxietyYes 1.249
## MoodDisordYes 0.980
## Chronicconditions -1.429
## PASE_TOTALbaseline 2.123
## Animal_Fluency_Normedbaseline 73.510
## timefactor2:PandemicFU2 data collected before COVID-19 -1.066
## timefactor2:Age_sexFemales 65+ -2.900
## timefactor2:Age_sexMales 45-64 -1.856
## timefactor2:Age_sexMales 65+ -4.109
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.047
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -2.001
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.450
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.691
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.088
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.194
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 6.73e-05
## PandemicFU2 data collected before COVID-19 0.002858
## Age_sexFemales 65+ 0.192633
## Age_sexMales 45-64 0.639637
## Age_sexMales 65+ 0.315683
## EducationHigh School Diploma 0.000762
## EducationLess than High School Diploma 0.147943
## EducationSome College 0.054616
## EthnicityWhite 0.000984
## IncomeLevel>$150k 0.925913
## IncomeLevel$100-150k 0.556871
## IncomeLevel$20-50k 0.102627
## IncomeLevel$50-100k 0.376371
## BMI 0.077033
## CESD.10baseline 0.082546
## SmokingStatusFormer Smoker 0.151702
## SmokingStatusNever Smoked 0.089131
## SmokingStatusOccasional Smoker 0.909018
## RelationshipstatusMarried 0.549624
## RelationshipstatusSeparated 0.224342
## RelationshipstatusSingle 0.543377
## RelationshipstatusWidowed 0.785017
## LivingstatusAssisted Living 0.419680
## LivingstatusHouse 0.035995
## LivingstatusOther 0.302767
## AnxietyYes 0.211862
## MoodDisordYes 0.327272
## Chronicconditions 0.153081
## PASE_TOTALbaseline 0.033754
## Animal_Fluency_Normedbaseline < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19 0.286512
## timefactor2:Age_sexFemales 65+ 0.003744
## timefactor2:Age_sexMales 45-64 0.063458
## timefactor2:Age_sexMales 65+ 4.02e-05
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.962216
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.045439
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.147142
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.489667
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.276693
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.028296
##
## (Intercept) ***
## timefactor2 ***
## PandemicFU2 data collected before COVID-19 **
## Age_sexFemales 65+
## Age_sexMales 45-64
## Age_sexMales 65+
## EducationHigh School Diploma ***
## EducationLess than High School Diploma
## EducationSome College .
## EthnicityWhite ***
## IncomeLevel>$150k
## IncomeLevel$100-150k
## IncomeLevel$20-50k
## IncomeLevel$50-100k
## BMI .
## CESD.10baseline .
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked .
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed
## LivingstatusAssisted Living
## LivingstatusHouse *
## LivingstatusOther
## AnxietyYes
## MoodDisordYes
## Chronicconditions
## PASE_TOTALbaseline *
## Animal_Fluency_Normedbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19
## timefactor2:Age_sexFemales 65+ **
## timefactor2:Age_sexMales 45-64 .
## timefactor2:Age_sexMales 65+ ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 *
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Significantly lower animal fluency for males and females 65+
lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.907504 0.1608920 Inf 9.592161 10.222846
## FU2 data collected before COVID-19 10.182323 0.1554464 Inf 9.877653 10.486992
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.219637 0.1613010 Inf 9.903493 10.535781
## FU2 data collected before COVID-19 10.388444 0.1554599 Inf 10.083748 10.693139
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.737693 0.1770956 Inf 9.390592 10.084794
## FU2 data collected before COVID-19 10.020029 0.1673864 Inf 9.691957 10.348100
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.656666 0.1781418 Inf 9.307514 10.005817
## FU2 data collected before COVID-19 9.951504 0.1675738 Inf 9.623065 10.279942
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.862574 0.1551117 Inf 9.558561 10.166588
## FU2 data collected before COVID-19 9.875764 0.1600020 Inf 9.562166 10.189362
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.985788 0.1552657 Inf 9.681473 10.290103
## FU2 data collected before COVID-19 10.046322 0.1598905 Inf 9.732942 10.359701
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.785402 0.1748902 Inf 9.442624 10.128181
## FU2 data collected before COVID-19 9.836489 0.1661219 Inf 9.510896 10.162082
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.565060 0.1760872 Inf 9.219935 9.910184
## FU2 data collected before COVID-19 9.876957 0.1661718 Inf 9.551266 10.202647
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.27481878 0.09212404 Inf -2.983 0.0029
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.16880671 0.09298717 Inf -1.815 0.0695
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.28233582 0.12944447 Inf -2.181 0.0292
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.29483786 0.13123052 Inf -2.247 0.0247
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.01318983 0.09317313 Inf -0.142 0.8874
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.06053375 0.09322035 Inf -0.649 0.5161
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.05108655 0.12398229 Inf -0.412 0.6803
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.31189693 0.12559983 Inf -2.483 0.0130
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.27481878 0.09212404 Inf -0.4553786 -0.09425898
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.16880671 0.09298717 Inf -0.3510582 0.01344479
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.28233582 0.12944447 Inf -0.5360423 -0.02862933
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.29483786 0.13123052 Inf -0.5520450 -0.03763076
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.01318983 0.09317313 Inf -0.1958058 0.16942616
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.06053375 0.09322035 Inf -0.2432423 0.12217477
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.05108655 0.12398229 Inf -0.2940874 0.19191427
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.31189693 0.12559983 Inf -0.5580681 -0.06572578
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
Animals_lsmeans_adj11 <- summary(lsmeans(modelAnimals_adj11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 16543' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 16543)' or larger];
## but be warned that this may result in large computation time and memory use.
Animals_lsmeans_adj11$Time<-NA
Animals_lsmeans_adj11$Time[Animals_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj11$Time[Animals_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
modelPASE_1<- lmer(PASE_TOTAL~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
(1|ID), data= Tracking.data_long)
summary(modelPASE_1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PASE_TOTAL ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 142465.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5047 -0.5346 -0.0521 0.4931 6.6277
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1695 41.17
## Residual 2574 50.73
## Number of obs: 12799, groups: ID, 8921
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 3.572e+02 8.182e+00
## timefactor2 -3.314e+00 2.017e+00
## timefactor3 -2.242e+01 2.091e+00
## PandemicFU2 data collected before COVID-19 -7.520e+00 1.449e+00
## Age -3.376e+00 7.809e-02
## SexM 2.195e+01 1.423e+00
## EducationHigh School Diploma 2.466e-01 2.050e+00
## EducationLess than High School Diploma 3.812e+00 2.940e+00
## EducationSome College 8.714e+00 2.514e+00
## EthnicityWhite 4.642e+00 3.979e+00
## IncomeLevel>$150k 3.521e+01 3.716e+00
## IncomeLevel$100-150k 3.598e+01 2.986e+00
## IncomeLevel$20-50k 1.608e+01 2.017e+00
## IncomeLevel$50-100k 2.575e+01 2.154e+00
## BMI -6.881e-01 1.379e-01
## CESD.20.1 -4.974e-01 1.627e-01
## SmokingStatusFormer Smoker 5.234e-01 2.805e+00
## SmokingStatusNever Smoked 5.746e+00 2.915e+00
## SmokingStatusOccasional Smoker 2.039e+00 5.482e+00
## RelationshipstatusMarried -8.902e+00 2.353e+00
## RelationshipstatusSeparated -2.665e+00 4.649e+00
## RelationshipstatusSingle -1.529e+01 3.192e+00
## RelationshipstatusWidowed -4.304e+00 3.221e+00
## LivingstatusAssisted Living -1.978e-01 9.693e+00
## LivingstatusHouse 2.472e+01 2.115e+00
## LivingstatusOther 2.751e+01 7.935e+00
## AnxietyYes -6.175e+00 2.831e+00
## MoodDisordYes -8.554e+00 2.049e+00
## Chronicconditions -1.757e+00 3.310e-01
## timefactor2:PandemicFU2 data collected before COVID-19 -4.649e+00 2.799e+00
## timefactor3:PandemicFU2 data collected before COVID-19 1.070e+01 2.719e+00
## df t value
## (Intercept) 8.771e+03 43.656
## timefactor2 7.076e+03 -1.643
## timefactor3 7.083e+03 -10.723
## PandemicFU2 data collected before COVID-19 1.172e+04 -5.190
## Age 8.558e+03 -43.238
## SexM 8.482e+03 15.420
## EducationHigh School Diploma 8.767e+03 0.120
## EducationLess than High School Diploma 9.273e+03 1.297
## EducationSome College 8.396e+03 3.466
## EthnicityWhite 8.726e+03 1.167
## IncomeLevel>$150k 8.286e+03 9.474
## IncomeLevel$100-150k 8.338e+03 12.048
## IncomeLevel$20-50k 8.728e+03 7.971
## IncomeLevel$50-100k 8.600e+03 11.952
## BMI 8.990e+03 -4.990
## CESD.20.1 8.642e+03 -3.057
## SmokingStatusFormer Smoker 8.971e+03 0.187
## SmokingStatusNever Smoked 8.941e+03 1.971
## SmokingStatusOccasional Smoker 8.307e+03 0.372
## RelationshipstatusMarried 8.422e+03 -3.784
## RelationshipstatusSeparated 8.459e+03 -0.573
## RelationshipstatusSingle 8.543e+03 -4.791
## RelationshipstatusWidowed 8.609e+03 -1.336
## LivingstatusAssisted Living 8.192e+03 -0.020
## LivingstatusHouse 8.476e+03 11.692
## LivingstatusOther 9.074e+03 3.468
## AnxietyYes 8.569e+03 -2.181
## MoodDisordYes 8.431e+03 -4.174
## Chronicconditions 8.478e+03 -5.308
## timefactor2:PandemicFU2 data collected before COVID-19 7.033e+03 -1.661
## timefactor3:PandemicFU2 data collected before COVID-19 7.006e+03 3.937
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 0.100471
## timefactor3 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 2.14e-07 ***
## Age < 2e-16 ***
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.904230
## EducationLess than High School Diploma 0.194722
## EducationSome College 0.000532 ***
## EthnicityWhite 0.243431
## IncomeLevel>$150k < 2e-16 ***
## IncomeLevel$100-150k < 2e-16 ***
## IncomeLevel$20-50k 1.78e-15 ***
## IncomeLevel$50-100k < 2e-16 ***
## BMI 6.14e-07 ***
## CESD.20.1 0.002243 **
## SmokingStatusFormer Smoker 0.851996
## SmokingStatusNever Smoked 0.048740 *
## SmokingStatusOccasional Smoker 0.709947
## RelationshipstatusMarried 0.000156 ***
## RelationshipstatusSeparated 0.566408
## RelationshipstatusSingle 1.69e-06 ***
## RelationshipstatusWidowed 0.181504
## LivingstatusAssisted Living 0.983718
## LivingstatusHouse < 2e-16 ***
## LivingstatusOther 0.000528 ***
## AnxietyYes 0.029176 *
## MoodDisordYes 3.02e-05 ***
## Chronicconditions 1.14e-07 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.096708 .
## timefactor3:PandemicFU2 data collected before COVID-19 8.34e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Significant differences for males 45-64 and 65+, whereby post-pandemic males performed worse
lsmeans(modelPASE_1, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 166.0147 4.344428 Inf 157.4997 174.5296
## FU2 data collected before COVID-19 158.4949 4.352155 Inf 149.9649 167.0250
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 162.7005 4.650760 Inf 153.5852 171.8159
## FU2 data collected before COVID-19 150.5317 4.645490 Inf 141.4267 159.6367
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 143.5907 4.677721 Inf 134.4226 152.7589
## FU2 data collected before COVID-19 146.7748 4.558379 Inf 137.8406 155.7091
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelPASE_1, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 7.519720 1.448962 Inf 5.190 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 12.168875 2.707676 Inf 4.494 <.0001
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -3.184105 2.624720 Inf -1.213 0.2251
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelPASE_1, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 7.519720 1.448962 Inf 4.679806 10.359634
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 12.168875 2.707676 Inf 6.861928 17.475822
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -3.184105 2.624720 Inf -8.328461 1.960251
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
PASE_lsmeans_1 <- summary(lsmeans(modelPASE_1, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
PASE_lsmeans_1$Time<-NA
PASE_lsmeans_1$Time[PASE_lsmeans_1$timefactor==1]<-"Baseline"
PASE_lsmeans_1$Time[PASE_lsmeans_1$timefactor==2]<-"Follow-up 1"
PASE_lsmeans_1$Time[PASE_lsmeans_1$timefactor==3]<-"Follow-up 2"
ggplot(PASE_lsmeans_1, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from Baseline to FU2 by Pandemic status") +
theme_bw()
lsmeans.PASEtrun <- lsmeans(modelPASE_1, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.PASEtrun,list(c1st,c2nd,c3rd),by=NULL)
## contrast estimate SE df
## structure(c(-1, 1, 1, -1, 0, 0), dim = c(6L, 1L)) 4.649155 2.798586 Inf
## structure(c(0, 0, -1, 1, 1, -1), dim = c(6L, 1L)) -15.352980 3.463034 Inf
## structure(c(-1, 1, 0, 0, 1, -1), dim = c(6L, 1L)) -10.703825 2.719007 Inf
## z.ratio p.value
## 1.661 0.0967
## -4.433 <.0001
## -3.937 0.0001
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
modelPASE_2<- lmer(PASE_TOTAL ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelPASE_2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PASE_TOTAL ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 41379.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6919 -0.5399 -0.0547 0.5147 4.6443
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1120 33.47
## Residual 2268 47.62
## Number of obs: 3793, groups: ID, 3084
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 227.04329 14.48019
## timefactor2 -19.57979 2.64778
## PandemicFU2 data collected before COVID-19 -8.30427 2.75447
## Age -2.28226 0.13554
## SexM 19.08687 2.26456
## EducationHigh School Diploma -10.28018 3.35721
## EducationLess than High School Diploma -2.60637 5.33767
## EducationSome College 5.82881 3.88524
## EthnicityWhite 15.65811 6.50262
## IncomeLevel>$150k 10.13841 5.72612
## IncomeLevel$100-150k 13.35465 4.63478
## IncomeLevel$20-50k 6.55322 3.32002
## IncomeLevel$50-100k 10.32450 3.48386
## BMI -0.44277 0.23659
## CESD.10baseline 0.17110 0.26176
## SmokingStatusFormer Smoker -2.90890 4.82660
## SmokingStatusNever Smoked -0.02248 4.98545
## SmokingStatusOccasional Smoker -7.94910 8.41014
## RelationshipstatusMarried -3.59607 3.63034
## RelationshipstatusSeparated 2.93953 7.20676
## RelationshipstatusSingle -6.34248 5.06510
## RelationshipstatusWidowed -0.74498 5.15533
## LivingstatusAssisted Living -13.91674 14.31955
## LivingstatusHouse 13.00541 3.33789
## LivingstatusOther 34.09675 13.60157
## AnxietyYes -2.17396 4.50546
## MoodDisordYes -9.86520 3.18407
## Chronicconditions -1.60979 0.52194
## PASE_TOTALbaseline 0.36667 0.01506
## timefactor2:PandemicFU2 data collected before COVID-19 15.51764 3.52774
## df t value
## (Intercept) 2954.43057 15.680
## timefactor2 2275.73813 -7.395
## PandemicFU2 data collected before COVID-19 3750.05357 -3.015
## Age 2936.56481 -16.838
## SexM 2908.58872 8.429
## EducationHigh School Diploma 2969.33237 -3.062
## EducationLess than High School Diploma 2949.45310 -0.488
## EducationSome College 2890.33885 1.500
## EthnicityWhite 3166.30903 2.408
## IncomeLevel>$150k 2905.39647 1.771
## IncomeLevel$100-150k 2902.75839 2.881
## IncomeLevel$20-50k 2829.44662 1.974
## IncomeLevel$50-100k 2840.93470 2.964
## BMI 2952.58643 -1.871
## CESD.10baseline 2907.41624 0.654
## SmokingStatusFormer Smoker 2912.14471 -0.603
## SmokingStatusNever Smoked 2910.19409 -0.005
## SmokingStatusOccasional Smoker 2814.23613 -0.945
## RelationshipstatusMarried 2953.60642 -0.991
## RelationshipstatusSeparated 2818.72500 0.408
## RelationshipstatusSingle 2850.65384 -1.252
## RelationshipstatusWidowed 2905.49242 -0.145
## LivingstatusAssisted Living 3009.03622 -0.972
## LivingstatusHouse 2927.99248 3.896
## LivingstatusOther 3066.72319 2.507
## AnxietyYes 2935.07572 -0.483
## MoodDisordYes 2943.60783 -3.098
## Chronicconditions 2856.90903 -3.084
## PASE_TOTALbaseline 2899.10049 24.341
## timefactor2:PandemicFU2 data collected before COVID-19 2199.00680 4.399
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 1.98e-13 ***
## PandemicFU2 data collected before COVID-19 0.00259 **
## Age < 2e-16 ***
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.00222 **
## EducationLess than High School Diploma 0.62538
## EducationSome College 0.13366
## EthnicityWhite 0.01610 *
## IncomeLevel>$150k 0.07674 .
## IncomeLevel$100-150k 0.00399 **
## IncomeLevel$20-50k 0.04850 *
## IncomeLevel$50-100k 0.00307 **
## BMI 0.06138 .
## CESD.10baseline 0.51340
## SmokingStatusFormer Smoker 0.54677
## SmokingStatusNever Smoked 0.99640
## SmokingStatusOccasional Smoker 0.34465
## RelationshipstatusMarried 0.32198
## RelationshipstatusSeparated 0.68339
## RelationshipstatusSingle 0.21060
## RelationshipstatusWidowed 0.88511
## LivingstatusAssisted Living 0.33119
## LivingstatusHouse 9.99e-05 ***
## LivingstatusOther 0.01223 *
## AnxietyYes 0.62947
## MoodDisordYes 0.00196 **
## Chronicconditions 0.00206 **
## PASE_TOTALbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 1.14e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelPASE_2, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 159.7396 7.176628 Inf 145.6737 173.8055
## FU2 data collected before COVID-19 151.4353 7.141678 Inf 137.4379 165.4327
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 140.1598 7.146012 Inf 126.1539 154.1657
## FU2 data collected before COVID-19 147.3732 7.099894 Inf 133.4576 161.2887
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelPASE_2, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 8.304269 2.754465 Inf 3.015 0.0026
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -7.213373 2.668837 Inf -2.703 0.0069
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelPASE_2, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 8.304269 2.754465 Inf 2.905616 13.702921
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -7.213373 2.668837 Inf -12.444198 -1.982547
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
PASE_lsmeans_2 <- summary(lsmeans(modelPASE_2, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
PASE_lsmeans_2$Time<-NA
PASE_lsmeans_2$Time[PASE_lsmeans_2$timefactor==1]<-"Follow-up 1"
PASE_lsmeans_2$Time[PASE_lsmeans_2$timefactor==2]<-"Follow-up 2"
ggplot(PASE_lsmeans_2, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
lsmeans.PASE2trun <- lsmeans(modelPASE_2, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
contrast(lsmeans.PASE2trun,list(c4th),by=NULL)
## contrast estimate SE df z.ratio
## structure(c(-1, 1, 1, -1), dim = c(4L, 1L)) -15.51764 3.52774 Inf -4.399
## p.value
## <.0001
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
modelPASE_3<- lmer(PASE_TOTAL ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
(1|ID), data= Tracking.data_long)
summary(modelPASE_3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PASE_TOTAL ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +
## IncomeLevel + BMI + CESD.20.1 + SmokingStatus + Relationshipstatus +
## Livingstatus + Anxiety + MoodDisord + Chronicconditions + (1 | ID)
## Data: Tracking.data_long
##
## REML criterion at convergence: 143056.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5215 -0.5361 -0.0443 0.4883 6.0745
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2032 45.07
## Residual 2545 50.45
## Number of obs: 12799, groups: ID, 8921
##
## Fixed effects:
## Estimate
## (Intercept) 167.7452
## timefactor2 -0.5983
## timefactor3 -26.3636
## PandemicFU2 data collected before COVID-19 -11.2140
## Age_sexFemales 65+ -45.8363
## Age_sexMales 45-64 24.5914
## Age_sexMales 65+ -41.1588
## EducationHigh School Diploma -0.8631
## EducationLess than High School Diploma -0.7466
## EducationSome College 6.0380
## EthnicityWhite 0.4625
## IncomeLevel>$150k 40.8591
## IncomeLevel$100-150k 42.0589
## IncomeLevel$20-50k 16.8643
## IncomeLevel$50-100k 29.7152
## BMI -0.4639
## CESD.20.1 -0.2710
## SmokingStatusFormer Smoker -3.1718
## SmokingStatusNever Smoked 2.9083
## SmokingStatusOccasional Smoker 1.6958
## RelationshipstatusMarried -7.2289
## RelationshipstatusSeparated 1.8509
## RelationshipstatusSingle -12.2730
## RelationshipstatusWidowed -17.2244
## LivingstatusAssisted Living -12.5590
## LivingstatusHouse 28.8222
## LivingstatusOther 32.8348
## AnxietyYes -2.5781
## MoodDisordYes -6.9188
## Chronicconditions -3.2665
## timefactor2:PandemicFU2 data collected before COVID-19 -9.4841
## timefactor3:PandemicFU2 data collected before COVID-19 12.8291
## timefactor2:Age_sexFemales 65+ -10.0334
## timefactor3:Age_sexFemales 65+ 0.3101
## timefactor2:Age_sexMales 45-64 -5.7577
## timefactor3:Age_sexMales 45-64 8.9975
## timefactor2:Age_sexMales 65+ 4.5436
## timefactor3:Age_sexMales 65+ -1.2600
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 4.8711
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -4.2464
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 8.1284
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 13.4949
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.3230
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 12.0617
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -9.5731
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -4.8563
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 11.0099
## Std. Error
## (Intercept) 7.0918
## timefactor2 3.6104
## timefactor3 3.6841
## PandemicFU2 data collected before COVID-19 2.6073
## Age_sexFemales 65+ 3.6999
## Age_sexMales 45-64 2.7106
## Age_sexMales 65+ 3.4435
## EducationHigh School Diploma 2.1395
## EducationLess than High School Diploma 3.0637
## EducationSome College 2.6263
## EthnicityWhite 4.1509
## IncomeLevel>$150k 3.8779
## IncomeLevel$100-150k 3.1104
## IncomeLevel$20-50k 2.1070
## IncomeLevel$50-100k 2.2476
## BMI 0.1436
## CESD.20.1 0.1696
## SmokingStatusFormer Smoker 2.9232
## SmokingStatusNever Smoked 3.0407
## SmokingStatusOccasional Smoker 5.7271
## RelationshipstatusMarried 2.4628
## RelationshipstatusSeparated 4.8541
## RelationshipstatusSingle 3.3341
## RelationshipstatusWidowed 3.3483
## LivingstatusAssisted Living 10.1264
## LivingstatusHouse 2.2055
## LivingstatusOther 8.2758
## AnxietyYes 2.9531
## MoodDisordYes 2.1419
## Chronicconditions 0.3416
## timefactor2:PandemicFU2 data collected before COVID-19 4.7623
## timefactor3:PandemicFU2 data collected before COVID-19 4.5913
## timefactor2:Age_sexFemales 65+ 6.6896
## timefactor3:Age_sexFemales 65+ 6.9388
## timefactor2:Age_sexMales 45-64 4.8472
## timefactor3:Age_sexMales 45-64 4.9372
## timefactor2:Age_sexMales 65+ 6.2276
## timefactor3:Age_sexMales 65+ 6.7673
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 4.5033
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 3.7024
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 4.3782
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 8.5848
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 8.4820
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 7.0564
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 6.7285
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 8.3550
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 8.4192
## df
## (Intercept) 9169.0045
## timefactor2 6631.4472
## timefactor3 6592.9808
## PandemicFU2 data collected before COVID-19 11653.5061
## Age_sexFemales 65+ 11445.8943
## Age_sexMales 45-64 11588.9293
## Age_sexMales 65+ 11493.7519
## EducationHigh School Diploma 8879.4010
## EducationLess than High School Diploma 9312.3854
## EducationSome College 8545.2622
## EthnicityWhite 8828.2245
## IncomeLevel>$150k 8440.8765
## IncomeLevel$100-150k 8484.3413
## IncomeLevel$20-50k 8843.3563
## IncomeLevel$50-100k 8731.4268
## BMI 9082.8962
## CESD.20.1 8768.7720
## SmokingStatusFormer Smoker 9059.6409
## SmokingStatusNever Smoked 9032.1957
## SmokingStatusOccasional Smoker 8465.0517
## RelationshipstatusMarried 8590.9892
## RelationshipstatusSeparated 8603.4254
## RelationshipstatusSingle 8687.9964
## RelationshipstatusWidowed 8726.3521
## LivingstatusAssisted Living 8356.3302
## LivingstatusHouse 8608.7322
## LivingstatusOther 9155.5462
## AnxietyYes 8698.9296
## MoodDisordYes 8572.1997
## Chronicconditions 8651.3642
## timefactor2:PandemicFU2 data collected before COVID-19 6628.3066
## timefactor3:PandemicFU2 data collected before COVID-19 6502.7641
## timefactor2:Age_sexFemales 65+ 6871.8670
## timefactor3:Age_sexFemales 65+ 7005.1325
## timefactor2:Age_sexMales 45-64 6764.5832
## timefactor3:Age_sexMales 45-64 6740.1311
## timefactor2:Age_sexMales 65+ 6643.0545
## timefactor3:Age_sexMales 65+ 6661.6252
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 11658.0478
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 11634.4681
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 11602.3606
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 6796.9877
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 6920.7602
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 6704.0699
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 6620.6272
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 6688.7036
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 6673.4271
## t value
## (Intercept) 23.653
## timefactor2 -0.166
## timefactor3 -7.156
## PandemicFU2 data collected before COVID-19 -4.301
## Age_sexFemales 65+ -12.388
## Age_sexMales 45-64 9.072
## Age_sexMales 65+ -11.953
## EducationHigh School Diploma -0.403
## EducationLess than High School Diploma -0.244
## EducationSome College 2.299
## EthnicityWhite 0.111
## IncomeLevel>$150k 10.537
## IncomeLevel$100-150k 13.522
## IncomeLevel$20-50k 8.004
## IncomeLevel$50-100k 13.221
## BMI -3.231
## CESD.20.1 -1.597
## SmokingStatusFormer Smoker -1.085
## SmokingStatusNever Smoked 0.956
## SmokingStatusOccasional Smoker 0.296
## RelationshipstatusMarried -2.935
## RelationshipstatusSeparated 0.381
## RelationshipstatusSingle -3.681
## RelationshipstatusWidowed -5.144
## LivingstatusAssisted Living -1.240
## LivingstatusHouse 13.069
## LivingstatusOther 3.968
## AnxietyYes -0.873
## MoodDisordYes -3.230
## Chronicconditions -9.562
## timefactor2:PandemicFU2 data collected before COVID-19 -1.991
## timefactor3:PandemicFU2 data collected before COVID-19 2.794
## timefactor2:Age_sexFemales 65+ -1.500
## timefactor3:Age_sexFemales 65+ 0.045
## timefactor2:Age_sexMales 45-64 -1.188
## timefactor3:Age_sexMales 45-64 1.822
## timefactor2:Age_sexMales 65+ 0.730
## timefactor3:Age_sexMales 65+ -0.186
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.082
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.147
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.857
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.572
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.038
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.709
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.423
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -0.581
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.308
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 0.868375
## timefactor3 9.21e-13
## PandemicFU2 data collected before COVID-19 1.71e-05
## Age_sexFemales 65+ < 2e-16
## Age_sexMales 45-64 < 2e-16
## Age_sexMales 65+ < 2e-16
## EducationHigh School Diploma 0.686668
## EducationLess than High School Diploma 0.807462
## EducationSome College 0.021527
## EthnicityWhite 0.911280
## IncomeLevel>$150k < 2e-16
## IncomeLevel$100-150k < 2e-16
## IncomeLevel$20-50k 1.36e-15
## IncomeLevel$50-100k < 2e-16
## BMI 0.001239
## CESD.20.1 0.110193
## SmokingStatusFormer Smoker 0.277932
## SmokingStatusNever Smoked 0.338860
## SmokingStatusOccasional Smoker 0.767163
## RelationshipstatusMarried 0.003341
## RelationshipstatusSeparated 0.702977
## RelationshipstatusSingle 0.000234
## RelationshipstatusWidowed 2.74e-07
## LivingstatusAssisted Living 0.214927
## LivingstatusHouse < 2e-16
## LivingstatusOther 7.32e-05
## AnxietyYes 0.382680
## MoodDisordYes 0.001241
## Chronicconditions < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19 0.046467
## timefactor3:PandemicFU2 data collected before COVID-19 0.005218
## timefactor2:Age_sexFemales 65+ 0.133699
## timefactor3:Age_sexFemales 65+ 0.964360
## timefactor2:Age_sexMales 45-64 0.234937
## timefactor3:Age_sexMales 45-64 0.068438
## timefactor2:Age_sexMales 65+ 0.465668
## timefactor3:Age_sexMales 65+ 0.852301
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.279415
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.251440
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.063395
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.116007
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.969627
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.087438
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.154853
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.561095
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.191016
##
## (Intercept) ***
## timefactor2
## timefactor3 ***
## PandemicFU2 data collected before COVID-19 ***
## Age_sexFemales 65+ ***
## Age_sexMales 45-64 ***
## Age_sexMales 65+ ***
## EducationHigh School Diploma
## EducationLess than High School Diploma
## EducationSome College *
## EthnicityWhite
## IncomeLevel>$150k ***
## IncomeLevel$100-150k ***
## IncomeLevel$20-50k ***
## IncomeLevel$50-100k ***
## BMI **
## CESD.20.1
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried **
## RelationshipstatusSeparated
## RelationshipstatusSingle ***
## RelationshipstatusWidowed ***
## LivingstatusAssisted Living
## LivingstatusHouse ***
## LivingstatusOther ***
## AnxietyYes
## MoodDisordYes **
## Chronicconditions ***
## timefactor2:PandemicFU2 data collected before COVID-19 *
## timefactor3:PandemicFU2 data collected before COVID-19 **
## timefactor2:Age_sexFemales 65+
## timefactor3:Age_sexFemales 65+
## timefactor2:Age_sexMales 45-64
## timefactor3:Age_sexMales 45-64 .
## timefactor2:Age_sexMales 65+
## timefactor3:Age_sexMales 65+
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ .
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 .
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 47 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Significant differences for males 65+
lsmeans(modelPASE_3, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 173.4374 4.876716 Inf 163.87919 182.9956
## FU2 data collected before COVID-19 162.2234 4.740156 Inf 152.93285 171.5139
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 172.8390 5.648363 Inf 161.76845 183.9096
## FU2 data collected before COVID-19 152.1409 5.383113 Inf 141.59019 162.6916
##
## timefactor = 3, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 147.0738 5.710253 Inf 135.88194 158.2657
## FU2 data collected before COVID-19 148.6890 5.188934 Inf 138.51883 158.8591
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 127.6010 5.302595 Inf 117.20814 137.9939
## FU2 data collected before COVID-19 121.2582 5.045488 Inf 111.36921 131.1472
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 116.9693 7.080697 Inf 103.09136 130.8472
## FU2 data collected before COVID-19 114.6372 6.231594 Inf 102.42349 126.8509
##
## timefactor = 3, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 101.5476 7.197271 Inf 87.44116 115.6539
## FU2 data collected before COVID-19 108.3568 5.930499 Inf 96.73323 119.9804
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 198.0288 4.715543 Inf 188.78647 207.2711
## FU2 data collected before COVID-19 182.5684 4.859823 Inf 173.04331 192.0935
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 191.6727 5.408226 Inf 181.07278 202.2726
## FU2 data collected before COVID-19 178.7899 5.961582 Inf 167.10538 190.4743
##
## timefactor = 3, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 180.6627 5.445629 Inf 169.98948 191.3359
## FU2 data collected before COVID-19 168.4584 5.702579 Inf 157.28156 179.6353
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 132.2786 5.257342 Inf 121.97436 142.5828
## FU2 data collected before COVID-19 129.1930 5.010991 Inf 119.37165 139.0144
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 136.2238 6.695998 Inf 123.09991 149.3477
## FU2 data collected before COVID-19 118.7978 6.347852 Inf 106.35629 131.2394
##
## timefactor = 3, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 104.6550 7.176004 Inf 90.59029 118.7197
## FU2 data collected before COVID-19 125.4085 6.017013 Inf 113.61532 137.2016
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelPASE_3, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 11.213990 2.607303 Inf 4.301 <.0001
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 20.698138 4.615920 Inf 4.484 <.0001
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -1.615127 4.448075 Inf -0.363 0.7165
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 6.342850 3.678247 Inf 1.724 0.0846
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 2.332081 6.948368 Inf 0.336 0.7371
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -6.809245 6.916113 Inf -0.985 0.3248
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 15.460376 2.635604 Inf 5.866 <.0001
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 12.882844 5.079743 Inf 2.536 0.0112
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 12.204310 4.784730 Inf 2.551 0.0108
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 3.085549 3.521855 Inf 0.876 0.3810
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 17.425972 6.699385 Inf 2.601 0.0093
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -20.753451 6.891802 Inf -3.011 0.0026
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelPASE_3, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 11.213990 2.607303 Inf 6.10377 16.324209
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 20.698138 4.615920 Inf 11.65110 29.745174
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -1.615127 4.448075 Inf -10.33319 7.102940
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 6.342850 3.678247 Inf -0.86638 13.552082
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 2.332081 6.948368 Inf -11.28647 15.950631
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -6.809245 6.916113 Inf -20.36458 6.746087
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 15.460376 2.635604 Inf 10.29469 20.626066
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 12.882844 5.079743 Inf 2.92673 22.838958
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 12.204310 4.784730 Inf 2.82641 21.582209
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 3.085549 3.521855 Inf -3.81716 9.988258
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 17.425972 6.699385 Inf 4.29542 30.556526
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -20.753451 6.891802 Inf -34.26113 -7.245767
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
PASE_lsmeans_3 <- summary(lsmeans(modelPASE_3, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12799' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12799)' or larger];
## but be warned that this may result in large computation time and memory use.
PASE_lsmeans_3$Time<-NA
PASE_lsmeans_3$Time[PASE_lsmeans_3$timefactor==1]<-"Baseline"
PASE_lsmeans_3$Time[PASE_lsmeans_3$timefactor==2]<-"Follow-up 1"
PASE_lsmeans_3$Time[PASE_lsmeans_3$timefactor==3]<-"Follow-up 2"
ggplot(PASE_lsmeans_3, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "MAT Score", title = "PASE Total Score from Baseline to FU2 by Pandemic status") +
theme_bw()
modelPASE_4<- lmer(PASE_TOTAL ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= Tracking.data_long_2)
summary(modelPASE_4)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PASE_TOTAL ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +
## IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
## Livingstatus + Anxiety + MoodDisord + Chronicconditions +
## PASE_TOTALbaseline + (1 | ID)
## Data: Tracking.data_long_2
##
## REML criterion at convergence: 41443.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5900 -0.5394 -0.0518 0.4975 4.3962
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1281 35.79
## Residual 2248 47.41
## Number of obs: 3793, groups: ID, 3084
##
## Fixed effects:
## Estimate
## (Intercept) 93.33664
## timefactor2 -25.70659
## PandemicFU2 data collected before COVID-19 -13.80217
## Age_sexFemales 65+ -35.79615
## Age_sexMales 45-64 13.17052
## Age_sexMales 65+ -17.99018
## EducationHigh School Diploma -11.03172
## EducationLess than High School Diploma -6.09233
## EducationSome College 3.85447
## EthnicityWhite 12.53759
## IncomeLevel>$150k 11.95503
## IncomeLevel$100-150k 15.37351
## IncomeLevel$20-50k 5.44354
## IncomeLevel$50-100k 10.93393
## BMI -0.30116
## CESD.10baseline 0.33349
## SmokingStatusFormer Smoker -4.52178
## SmokingStatusNever Smoked -0.97084
## SmokingStatusOccasional Smoker -6.13003
## RelationshipstatusMarried -2.29172
## RelationshipstatusSeparated 5.86833
## RelationshipstatusSingle -3.53788
## RelationshipstatusWidowed -8.94497
## LivingstatusAssisted Living -21.55437
## LivingstatusHouse 15.13757
## LivingstatusOther 35.77027
## AnxietyYes -0.18008
## MoodDisordYes -8.86110
## Chronicconditions -2.46867
## PASE_TOTALbaseline 0.41899
## timefactor2:PandemicFU2 data collected before COVID-19 21.17509
## timefactor2:Age_sexFemales 65+ 12.89167
## timefactor2:Age_sexMales 45-64 12.00009
## timefactor2:Age_sexMales 65+ -5.32152
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 14.33558
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 6.24467
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.79579
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -13.94747
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -16.63398
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 15.90578
## Std. Error
## (Intercept) 11.92745
## timefactor2 4.63003
## PandemicFU2 data collected before COVID-19 4.66193
## Age_sexFemales 65+ 6.86117
## Age_sexMales 45-64 4.83736
## Age_sexMales 65+ 6.17899
## EducationHigh School Diploma 3.44383
## EducationLess than High School Diploma 5.46166
## EducationSome College 3.98186
## EthnicityWhite 6.65003
## IncomeLevel>$150k 5.86836
## IncomeLevel$100-150k 4.74613
## IncomeLevel$20-50k 3.40474
## IncomeLevel$50-100k 3.57438
## BMI 0.24216
## CESD.10baseline 0.26816
## SmokingStatusFormer Smoker 4.94836
## SmokingStatusNever Smoked 5.11375
## SmokingStatusOccasional Smoker 8.62339
## RelationshipstatusMarried 3.74714
## RelationshipstatusSeparated 7.39034
## RelationshipstatusSingle 5.20164
## RelationshipstatusWidowed 5.26013
## LivingstatusAssisted Living 14.67116
## LivingstatusHouse 3.41900
## LivingstatusOther 13.94851
## AnxietyYes 4.61549
## MoodDisordYes 3.26584
## Chronicconditions 0.53330
## PASE_TOTALbaseline 0.01477
## timefactor2:PandemicFU2 data collected before COVID-19 5.90005
## timefactor2:Age_sexFemales 65+ 8.85680
## timefactor2:Age_sexMales 45-64 6.27872
## timefactor2:Age_sexMales 65+ 8.33972
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 8.46202
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 6.89888
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 8.17141
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 10.99223
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 8.75135
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 10.76307
## df
## (Intercept) 3120.20879
## timefactor2 2051.15112
## PandemicFU2 data collected before COVID-19 3722.23633
## Age_sexFemales 65+ 3752.97886
## Age_sexMales 45-64 3751.25218
## Age_sexMales 65+ 3752.94868
## EducationHigh School Diploma 2985.93780
## EducationLess than High School Diploma 2964.93494
## EducationSome College 2911.26795
## EthnicityWhite 3160.35277
## IncomeLevel>$150k 2926.59990
## IncomeLevel$100-150k 2920.70539
## IncomeLevel$20-50k 2851.09864
## IncomeLevel$50-100k 2859.91393
## BMI 2968.34592
## CESD.10baseline 2925.64447
## SmokingStatusFormer Smoker 2930.08505
## SmokingStatusNever Smoked 2927.48640
## SmokingStatusOccasional Smoker 2840.80522
## RelationshipstatusMarried 2974.45173
## RelationshipstatusSeparated 2844.75947
## RelationshipstatusSingle 2874.59836
## RelationshipstatusWidowed 2923.73677
## LivingstatusAssisted Living 3021.54585
## LivingstatusHouse 2944.42575
## LivingstatusOther 3077.51156
## AnxietyYes 2951.85121
## MoodDisordYes 2962.52608
## Chronicconditions 2881.45429
## PASE_TOTALbaseline 2928.31196
## timefactor2:PandemicFU2 data collected before COVID-19 1987.84379
## timefactor2:Age_sexFemales 65+ 2134.11859
## timefactor2:Age_sexMales 45-64 2182.80706
## timefactor2:Age_sexMales 65+ 2278.93539
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 3737.95537
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 3725.37940
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3741.78771
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2087.59997
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2109.76182
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2269.82735
## t value
## (Intercept) 7.825
## timefactor2 -5.552
## PandemicFU2 data collected before COVID-19 -2.961
## Age_sexFemales 65+ -5.217
## Age_sexMales 45-64 2.723
## Age_sexMales 65+ -2.912
## EducationHigh School Diploma -3.203
## EducationLess than High School Diploma -1.115
## EducationSome College 0.968
## EthnicityWhite 1.885
## IncomeLevel>$150k 2.037
## IncomeLevel$100-150k 3.239
## IncomeLevel$20-50k 1.599
## IncomeLevel$50-100k 3.059
## BMI -1.244
## CESD.10baseline 1.244
## SmokingStatusFormer Smoker -0.914
## SmokingStatusNever Smoked -0.190
## SmokingStatusOccasional Smoker -0.711
## RelationshipstatusMarried -0.612
## RelationshipstatusSeparated 0.794
## RelationshipstatusSingle -0.680
## RelationshipstatusWidowed -1.701
## LivingstatusAssisted Living -1.469
## LivingstatusHouse 4.427
## LivingstatusOther 2.564
## AnxietyYes -0.039
## MoodDisordYes -2.713
## Chronicconditions -4.629
## PASE_TOTALbaseline 28.375
## timefactor2:PandemicFU2 data collected before COVID-19 3.589
## timefactor2:Age_sexFemales 65+ 1.456
## timefactor2:Age_sexMales 45-64 1.911
## timefactor2:Age_sexMales 65+ -0.638
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.694
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.905
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -0.220
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -1.269
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.901
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.478
## Pr(>|t|)
## (Intercept) 6.88e-15
## timefactor2 3.19e-08
## PandemicFU2 data collected before COVID-19 0.00309
## Age_sexFemales 65+ 1.91e-07
## Age_sexMales 45-64 0.00651
## Age_sexMales 65+ 0.00362
## EducationHigh School Diploma 0.00137
## EducationLess than High School Diploma 0.26474
## EducationSome College 0.33312
## EthnicityWhite 0.05948
## IncomeLevel>$150k 0.04172
## IncomeLevel$100-150k 0.00121
## IncomeLevel$20-50k 0.10997
## IncomeLevel$50-100k 0.00224
## BMI 0.21373
## CESD.10baseline 0.21374
## SmokingStatusFormer Smoker 0.36090
## SmokingStatusNever Smoked 0.84944
## SmokingStatusOccasional Smoker 0.47723
## RelationshipstatusMarried 0.54085
## RelationshipstatusSeparated 0.42723
## RelationshipstatusSingle 0.49647
## RelationshipstatusWidowed 0.08914
## LivingstatusAssisted Living 0.14189
## LivingstatusHouse 9.88e-06
## LivingstatusOther 0.01038
## AnxietyYes 0.96888
## MoodDisordYes 0.00670
## Chronicconditions 3.84e-06
## PASE_TOTALbaseline < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19 0.00034
## timefactor2:Age_sexFemales 65+ 0.14566
## timefactor2:Age_sexMales 45-64 0.05611
## timefactor2:Age_sexMales 65+ 0.52348
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.09033
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.36543
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.82607
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.20464
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.05747
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.13960
##
## (Intercept) ***
## timefactor2 ***
## PandemicFU2 data collected before COVID-19 **
## Age_sexFemales 65+ ***
## Age_sexMales 45-64 **
## Age_sexMales 65+ **
## EducationHigh School Diploma **
## EducationLess than High School Diploma
## EducationSome College
## EthnicityWhite .
## IncomeLevel>$150k *
## IncomeLevel$100-150k **
## IncomeLevel$20-50k
## IncomeLevel$50-100k **
## BMI
## CESD.10baseline
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed .
## LivingstatusAssisted Living
## LivingstatusHouse ***
## LivingstatusOther *
## AnxietyYes
## MoodDisordYes **
## Chronicconditions ***
## PASE_TOTALbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19 ***
## timefactor2:Age_sexFemales 65+
## timefactor2:Age_sexMales 45-64 .
## timefactor2:Age_sexMales 65+
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ .
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 .
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Significant differences for females 45-64 and 65+ and significant differences males 65+
lsmeans(modelPASE_4, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 164.3075 7.930214 Inf 148.76454 179.8504
## FU2 data collected before COVID-19 150.5053 7.703752 Inf 135.40623 165.6044
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 138.6009 7.954807 Inf 123.00974 154.1920
## FU2 data collected before COVID-19 145.9738 7.568732 Inf 131.13936 160.8082
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 128.5113 9.112584 Inf 110.65098 146.3716
## FU2 data collected before COVID-19 129.0447 8.440193 Inf 112.50226 145.5872
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 115.6964 9.126499 Inf 97.80878 133.5840
## FU2 data collected before COVID-19 123.4574 8.168835 Inf 107.44681 139.4681
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 177.4780 7.789159 Inf 162.21152 192.7445
## FU2 data collected before COVID-19 169.9205 8.093574 Inf 154.05738 185.7836
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 163.7715 7.771707 Inf 148.53922 179.0037
## FU2 data collected before COVID-19 160.7551 7.974000 Inf 145.12635 176.3839
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 146.3173 8.746042 Inf 129.17537 163.4592
## FU2 data collected before COVID-19 130.7193 8.426516 Inf 114.20367 147.2350
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 115.2892 9.125577 Inf 97.40338 133.1750
## FU2 data collected before COVID-19 136.7721 8.225615 Inf 120.65018 152.8940
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelPASE_4, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 13.802165 4.661931 Inf 2.961 0.0031
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -7.372924 4.451193 Inf -1.656 0.0976
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.533419 7.070369 Inf -0.075 0.9399
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -7.761034 7.180605 Inf -1.081 0.2798
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 7.557492 5.099919 Inf 1.482 0.1384
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 3.016380 4.797464 Inf 0.629 0.5295
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 15.597954 6.725774 Inf 2.319 0.0204
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -21.482911 6.934304 Inf -3.098 0.0019
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelPASE_4, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 13.802165 4.661931 Inf 4.66495 22.939382
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -7.372924 4.451193 Inf -16.09710 1.351255
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.533419 7.070369 Inf -14.39109 13.324250
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -7.761034 7.180605 Inf -21.83476 6.312694
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 7.557492 5.099919 Inf -2.43816 17.553149
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 3.016380 4.797464 Inf -6.38648 12.419236
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 15.597954 6.725774 Inf 2.41568 28.780228
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -21.482911 6.934304 Inf -35.07390 -7.891924
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
PASE_lsmeans_4 <- summary(lsmeans(modelPASE_4, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3793' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3793)' or larger];
## but be warned that this may result in large computation time and memory use.
PASE_lsmeans_4$Time<-NA
PASE_lsmeans_4$Time[PASE_lsmeans_4$timefactor==1]<-"Follow-up 1"
PASE_lsmeans_4$Time[PASE_lsmeans_4$timefactor==2]<-"Follow-up 2"
ggplot(PASE_lsmeans_4, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
Create binary variable for 4+ hrs/day of SB
Tracking.data_long_2$SB.binary <- as.factor(ifelse(Tracking.data_long_2$PASE_Sit==10, 1, 0))
Tracking.data_long_2$SBbaseline.binary <- as.factor(ifelse(Tracking.data_long_2$PASE_Sitbaseline==10, 1, 0))
sit1 <- glmer(
SB.binary ~ timefactor*Pandemic + Age + Sex + SBbaseline.binary + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
(1|ID),
data = Tracking.data_long_2,
family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 6.09947 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
sit1
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: SB.binary ~ timefactor * Pandemic + Age + Sex + SBbaseline.binary +
## Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +
## SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +
## MoodDisord + Chronicconditions + (1 | ID)
## Data: Tracking.data_long_2
## AIC BIC logLik deviance df.resid
## 25490.96 25736.57 -12714.48 25428.96 20362
## Random effects:
## Groups Name Std.Dev.
## ID (Intercept) 0.8892
## Number of obs: 20393, groups: ID, 10230
## Fixed Effects:
## (Intercept)
## -2.348903
## timefactor2
## 0.481092
## PandemicFU2 data collected before COVID-19
## 0.081017
## Age
## 0.015428
## SexM
## -0.134353
## SBbaseline.binary1
## 1.147014
## EducationHigh School Diploma
## -0.004636
## EducationLess than High School Diploma
## 0.097418
## EducationSome College
## -0.083815
## EthnicityWhite
## -0.130210
## IncomeLevel>$150k
## -0.184708
## IncomeLevel$100-150k
## -0.011710
## IncomeLevel$20-50k
## -0.141811
## IncomeLevel$50-100k
## -0.218146
## BMI
## 0.039803
## CESD.10baseline
## 0.012359
## SmokingStatusFormer Smoker
## -0.300883
## SmokingStatusNever Smoked
## -0.323008
## SmokingStatusOccasional Smoker
## -0.338149
## RelationshipstatusMarried
## -0.173398
## RelationshipstatusSeparated
## 0.098122
## RelationshipstatusSingle
## 0.067729
## RelationshipstatusWidowed
## 0.010810
## LivingstatusAssisted Living
## 0.296195
## LivingstatusHouse
## -0.232848
## LivingstatusOther
## -0.497680
## AnxietyYes
## -0.011266
## MoodDisordYes
## 0.104958
## Chronicconditions
## 0.037485
## timefactor2:PandemicFU2 data collected before COVID-19
## -0.439137
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 2 lme4 warnings
ggpredict(sit1, c("timefactor", "Pandemic"))
## # Predicted probabilities of SB.binary
##
## # Pandemic = FU2 data collected after COVID-19
##
## timefactor | Predicted | 95% CI
## -------------------------------------
## 1 | 0.46 | [0.38, 0.54]
## 2 | 0.58 | [0.50, 0.65]
##
## # Pandemic = FU2 data collected before COVID-19
##
## timefactor | Predicted | 95% CI
## -------------------------------------
## 1 | 0.48 | [0.40, 0.56]
## 2 | 0.49 | [0.41, 0.57]
##
## Adjusted for:
## * Age = 60.00
## * Sex = F
## * SBbaseline.binary = 0
## * Education = College Degree or Higher
## * Ethnicity = Other
## * IncomeLevel = <$20k
## * BMI = 27.52
## * CESD.10baseline = 4.87
## * SmokingStatus = Daily Smoker
## * Relationshipstatus = Divorced
## * Livingstatus = Apartment/Condo/Townhome
## * Anxiety = No
## * MoodDisord = No
## * Chronicconditions = 2.76
## * ID = 0 (population-level)
Mean Differences and 95% CIs
sit.test <- as.data.frame(ggpredict(sit1, c("timefactor", "Pandemic")))
mean.diff.1<-(subset(sit.test,x==1 & group == "FU2 data collected after COVID-19")$predicted -
subset(sit.test,x==1 & group == "FU2 data collected before COVID-19")$predicted)
se.1<-(sqrt(((subset(sit.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sit.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
UL.1<-mean.diff.1 + se.1*1.96
LL.1<-mean.diff.1 - se.1*1.96
mean.diff.2<-(subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$predicted -
subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$predicted)
se.2<-(sqrt(((subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
UL.2<-mean.diff.2 + se.2*1.96
LL.2<-mean.diff.2 - se.2*1.96
#Mean differences and LL and UL
mean.diff.1
## [1] -0.02017452
UL.1
## [1] 0.2930919
LL.1
## [1] -0.3334409
mean.diff.2
## [1] 0.088871
UL.2
## [1] 0.4021841
LL.2
## [1] -0.2244421
z-scores
z.1<- (subset(sit.test,x==1 & group == "FU2 data collected after COVID-19")$predicted -
subset(sit.test,x==1 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sit.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sit.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
z.2<- (subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$predicted -
subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sit.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sit.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
z.1
## [1] -0.1262251
z.2
## [1] 0.5559523
p-values for z-scores
2*pnorm(z.1, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 1.100446
2*pnorm(z.2, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.5782435
ggpredict(sit1, c("timefactor", "Pandemic")) %>% plot()
sit2 <- glmer(
SB.binary ~ timefactor*Pandemic*Age_sex + SBbaseline.binary + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
(1|ID),
data = Tracking.data_long_2,
family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.884797 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
sit2
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: SB.binary ~ timefactor * Pandemic * Age_sex + SBbaseline.binary +
## Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +
## SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +
## MoodDisord + Chronicconditions + (1 | ID)
## Data: Tracking.data_long_2
## AIC BIC logLik deviance df.resid
## 25497.29 25822.13 -12707.64 25415.29 20352
## Random effects:
## Groups Name Std.Dev.
## ID (Intercept) 0.901
## Number of obs: 20393, groups: ID, 10230
## Fixed Effects:
## (Intercept)
## -1.620686
## timefactor2
## 0.580652
## PandemicFU2 data collected before COVID-19
## 0.057124
## Age_sexFemales 65+
## 0.398934
## Age_sexMales 45-64
## -0.146265
## Age_sexMales 65+
## 0.303371
## SBbaseline.binary1
## 1.150707
## EducationHigh School Diploma
## -0.005042
## EducationLess than High School Diploma
## 0.096709
## EducationSome College
## -0.073105
## EthnicityWhite
## -0.054078
## IncomeLevel>$150k
## -0.206336
## IncomeLevel$100-150k
## -0.032560
## IncomeLevel$20-50k
## -0.162937
## IncomeLevel$50-100k
## -0.243375
## BMI
## 0.039126
## CESD.10baseline
## 0.011728
## SmokingStatusFormer Smoker
## -0.290040
## SmokingStatusNever Smoked
## -0.315386
## SmokingStatusOccasional Smoker
## -0.327601
## RelationshipstatusMarried
## -0.176033
## RelationshipstatusSeparated
## 0.061149
## RelationshipstatusSingle
## 0.068453
## RelationshipstatusWidowed
## 0.044016
## LivingstatusAssisted Living
## 0.309546
## LivingstatusHouse
## -0.239899
## LivingstatusOther
## -0.451692
## AnxietyYes
## -0.039052
## MoodDisordYes
## 0.112731
## Chronicconditions
## 0.042309
## timefactor2:PandemicFU2 data collected before COVID-19
## -0.342007
## timefactor2:Age_sexFemales 65+
## -0.141938
## timefactor2:Age_sexMales 45-64
## -0.060952
## timefactor2:Age_sexMales 65+
## -0.124570
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## -0.015392
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## 0.104367
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## 0.094448
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## -0.173610
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## -0.037797
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## -0.387304
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 2 lme4 warnings
ggpredict(sit2, c("Pandemic","timefactor","Age_sex"))
## # Predicted probabilities of SB.binary
##
## # timefactor = 1
## # Age_sex = Females 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.41 | [0.33, 0.49]
## FU2 data collected before COVID-19 | 0.42 | [0.35, 0.50]
##
## # timefactor = 2
## # Age_sex = Females 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.55 | [0.47, 0.63]
## FU2 data collected before COVID-19 | 0.48 | [0.40, 0.56]
##
## # timefactor = 1
## # Age_sex = Females 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.51 | [0.42, 0.60]
## FU2 data collected before COVID-19 | 0.52 | [0.43, 0.60]
##
## # timefactor = 2
## # Age_sex = Females 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.61 | [0.53, 0.70]
## FU2 data collected before COVID-19 | 0.50 | [0.41, 0.58]
##
## # timefactor = 1
## # Age_sex = Males 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.37 | [0.30, 0.45]
## FU2 data collected before COVID-19 | 0.41 | [0.33, 0.50]
##
## # timefactor = 2
## # Age_sex = Males 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.50 | [0.42, 0.58]
## FU2 data collected before COVID-19 | 0.45 | [0.36, 0.53]
##
## # timefactor = 1
## # Age_sex = Males 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.48 | [0.39, 0.57]
## FU2 data collected before COVID-19 | 0.52 | [0.44, 0.61]
##
## # timefactor = 2
## # Age_sex = Males 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.60 | [0.51, 0.68]
## FU2 data collected before COVID-19 | 0.45 | [0.37, 0.54]
##
## Adjusted for:
## * SBbaseline.binary = 0
## * Education = College Degree or Higher
## * Ethnicity = Other
## * IncomeLevel = <$20k
## * BMI = 27.52
## * CESD.10baseline = 4.87
## * SmokingStatus = Daily Smoker
## * Relationshipstatus = Divorced
## * Livingstatus = Apartment/Condo/Townhome
## * Anxiety = No
## * MoodDisord = No
## * Chronicconditions = 2.76
## * ID = 0 (population-level)
Mean differences and 95% CIs
sit.test2 <- as.data.frame(ggpredict(sit2, c("timefactor", "Pandemic", "Age_sex")))
#Females 45-64 years
mean.diff.Females.Young.1<-(subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
se.Females.Young.1<-(sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
UL.Females.Young.1<-mean.diff.Females.Young.1 + se.Females.Young.1*1.96
LL.Females.Young.1<-mean.diff.Females.Young.1 - se.Females.Young.1*1.96
mean.diff.Females.Young.2<-(subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
se.Females.Young.2<-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
UL.Females.Young.2<-mean.diff.Females.Young.2 + se.Females.Young.2*1.96
LL.Females.Young.2<-mean.diff.Females.Young.2 - se.Females.Young.2*1.96
#Females 65+ years
mean.diff.Females.Old.1<-(subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
se.Females.Old.1<-(sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
UL.Females.Old.1<-mean.diff.Females.Old.1 + se.Females.Old.1*1.96
LL.Females.Old.1<-mean.diff.Females.Old.1 - se.Females.Old.1*1.96
mean.diff.Females.Old.2<-(subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
se.Females.Old.2<-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
UL.Females.Old.2<-mean.diff.Females.Old.2 + se.Females.Old.2*1.96
LL.Females.Old.2<-mean.diff.Females.Old.2 - se.Females.Old.2*1.96
#Males 45-64 years
mean.diff.Males.Young.1<-(subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
se.Males.Young.1<-(sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
UL.Males.Young.1<-mean.diff.Males.Young.1 + se.Males.Young.1*1.96
LL.Males.Young.1<-mean.diff.Males.Young.1 - se.Males.Young.1*1.96
mean.diff.Males.Young.2<-(subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
se.Males.Young.2<-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
UL.Males.Young.2<-mean.diff.Males.Young.2 + se.Males.Young.2*1.96
LL.Males.Young.2<-mean.diff.Males.Young.2 - se.Males.Young.2*1.96
#Males 65+ years
mean.diff.Males.Old.1<-(subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
se.Males.Old.1<-(sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
UL.Males.Old.1<-mean.diff.Males.Old.1 + se.Males.Old.1*1.96
LL.Males.Old.1<-mean.diff.Males.Old.1 - se.Males.Old.1*1.96
mean.diff.Males.Old.2<-(subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
se.Males.Old.2<-(sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
UL.Males.Old.2<-mean.diff.Males.Old.2 + se.Males.Old.2*1.96
LL.Males.Old.2<-mean.diff.Males.Old.2 - se.Males.Old.2*1.96
#Mean differences and LL and UL
mean.diff.Females.Young.1
## [1] -0.01387204
UL.Females.Young.1
## [1] 0.3166355
LL.Females.Young.1
## [1] -0.3443796
mean.diff.Females.Young.2
## [1] 0.07101829
UL.Females.Young.2
## [1] 0.4004958
LL.Females.Young.2
## [1] -0.2584592
mean.diff.Females.Old.1
## [1] -0.01042611
UL.Females.Old.1
## [1] 0.3382217
LL.Females.Old.1
## [1] -0.3590739
mean.diff.Females.Old.2
## [1] 0.1163847
UL.Females.Old.2
## [1] 0.4654892
LL.Females.Old.2
## [1] -0.2327198
mean.diff.Males.Young.1
## [1] -0.03849999
UL.Males.Young.1
## [1] 0.2976257
LL.Males.Young.1
## [1] -0.3746256
mean.diff.Males.Young.2
## [1] 0.05437307
UL.Males.Young.2
## [1] 0.3894572
LL.Males.Young.2
## [1] -0.2807111
mean.diff.Males.Old.1
## [1] -0.0378739
UL.Males.Old.1
## [1] 0.3159752
LL.Males.Old.1
## [1] -0.391723
mean.diff.Males.Old.2
## [1] 0.1430834
UL.Males.Old.2
## [1] 0.4970302
LL.Males.Old.2
## [1] -0.2108635
z-scores
z.Females.Young.1 <- (subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
z.Females.Young.2 <- (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
z.Females.Old.1 <- (subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
z.Females.Old.2 <- (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
z.Males.Young.1 <- (subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
z.Males.Young.2 <- (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
z.Males.Old.1 <- (subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sit.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sit.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
z.Males.Old.2 <- (subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sit.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sit.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
#z-scores
z.Females.Young.1
## [1] -0.08226499
z.Females.Young.2
## [1] 0.4224746
z.Males.Young.1
## [1] -0.2244993
z.Males.Young.2
## [1] 0.3180431
z.Females.Old.1
## [1] -0.05861263
z.Females.Old.2
## [1] 0.653426
z.Males.Old.1
## [1] -0.2097867
z.Males.Old.2
## [1] 0.792332
p-values for z-scores
2*pnorm(z.Females.Young.1, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 1.065564
2*pnorm(z.Females.Young.2, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.6726787
2*pnorm(z.Males.Young.1, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 1.177631
2*pnorm(z.Males.Young.2, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.7504522
2*pnorm(z.Females.Old.1, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 1.046739
2*pnorm(z.Females.Old.2, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.5134817
2*pnorm(z.Males.Old.1, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 1.166166
2*pnorm(z.Males.Old.2, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.4281671
ggpredict(sit2, c("timefactor","Pandemic","Age_sex")) %>% plot()
sleep1 <- glmer(
RSTLS_Sleep ~ timefactor*Pandemic + Age + Sex + RSTLS_Sleepbaseline + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
(1|ID),
data = Tracking.data_long_2,
family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 1.36972 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
sleep1
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## RSTLS_Sleep ~ timefactor * Pandemic + Age + Sex + RSTLS_Sleepbaseline +
## Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +
## SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +
## MoodDisord + Chronicconditions + (1 | ID)
## Data: Tracking.data_long_2
## AIC BIC logLik deviance df.resid
## 24480.69 24726.86 -12209.35 24418.69 20731
## Random effects:
## Groups Name Std.Dev.
## ID (Intercept) 0.8947
## Number of obs: 20762, groups: ID, 10407
## Fixed Effects:
## (Intercept)
## -1.101766
## timefactor2
## -0.184424
## PandemicFU2 data collected before COVID-19
## -0.007853
## Age
## -0.011876
## SexM
## -0.209542
## RSTLS_Sleepbaseline
## 0.913048
## EducationHigh School Diploma
## 0.139834
## EducationLess than High School Diploma
## 0.224461
## EducationSome College
## 0.148781
## EthnicityWhite
## 0.159890
## IncomeLevel>$150k
## -0.062121
## IncomeLevel$100-150k
## 0.013985
## IncomeLevel$20-50k
## -0.044483
## IncomeLevel$50-100k
## 0.009227
## BMI
## 0.001927
## CESD.10baseline
## 0.063722
## SmokingStatusFormer Smoker
## 0.051360
## SmokingStatusNever Smoked
## -0.066645
## SmokingStatusOccasional Smoker
## -0.105624
## RelationshipstatusMarried
## -0.059996
## RelationshipstatusSeparated
## -0.383550
## RelationshipstatusSingle
## -0.216066
## RelationshipstatusWidowed
## -0.147863
## LivingstatusAssisted Living
## 0.206873
## LivingstatusHouse
## 0.140978
## LivingstatusOther
## -0.055422
## AnxietyYes
## -0.145088
## MoodDisordYes
## -0.032438
## Chronicconditions
## 0.086628
## timefactor2:PandemicFU2 data collected before COVID-19
## -0.011079
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 2 lme4 warnings
ggpredict(sleep1, c("timefactor", "Pandemic"))
## # Predicted probabilities of RSTLS_Sleep
##
## # Pandemic = FU2 data collected after COVID-19
##
## timefactor | Predicted | 95% CI
## -------------------------------------
## 1 | 0.29 | [0.22, 0.36]
## 2 | 0.25 | [0.19, 0.32]
##
## # Pandemic = FU2 data collected before COVID-19
##
## timefactor | Predicted | 95% CI
## -------------------------------------
## 1 | 0.28 | [0.22, 0.35]
## 2 | 0.25 | [0.19, 0.31]
##
## Adjusted for:
## * Age = 60.00
## * Sex = F
## * RSTLS_Sleepbaseline = 0.33
## * Education = College Degree or Higher
## * Ethnicity = Other
## * IncomeLevel = <$20k
## * BMI = 27.53
## * CESD.10baseline = 4.88
## * SmokingStatus = Daily Smoker
## * Relationshipstatus = Divorced
## * Livingstatus = Apartment/Condo/Townhome
## * Anxiety = No
## * MoodDisord = No
## * Chronicconditions = 2.76
## * ID = 0 (population-level)
Mean Differences and 95% CIs
sleep.test <- as.data.frame(ggpredict(sleep1, c("timefactor", "Pandemic")))
mean.diff.3<-(subset(sleep.test,x==1 & group == "FU2 data collected after COVID-19")$predicted -
subset(sleep.test,x==1 & group == "FU2 data collected before COVID-19")$predicted)
se.3<-(sqrt(((subset(sleep.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sleep.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
UL.3<-mean.diff.3 + se.3*1.96
LL.3<-mean.diff.3 - se.3*1.96
mean.diff.4<-(subset(sleep.test,x==2 & group == "FU2 data collected after COVID-19")$predicted -
subset(sleep.test,x==2 & group == "FU2 data collected before COVID-19")$predicted)
se.4<-(sqrt(((subset(sleep.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sleep.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
UL.4<-mean.diff.4 + se.4*1.96
LL.4<-mean.diff.4 - se.4*1.96
#Mean differences and LL and UL
mean.diff.3
## [1] 0.001602157
UL.3
## [1] 0.3240006
LL.3
## [1] -0.3207962
mean.diff.4
## [1] 0.003534739
UL.4
## [1] 0.3265404
LL.4
## [1] -0.319471
z-scores
z.3<- (subset(sleep.test,x==1 & group == "FU2 data collected after COVID-19")$predicted -
subset(sleep.test,x==1 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sleep.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sleep.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
z.4<- (subset(sleep.test,x==2 & group == "FU2 data collected after COVID-19")$predicted -
subset(sleep.test,x==2 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sleep.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sleep.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
#z-scores
z.3
## [1] 0.009740209
z.4
## [1] 0.02144881
p-values for z-scores
2*pnorm(z.3, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.9922286
2*pnorm(z.4, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.9828876
ggpredict(sleep1, c("timefactor", "Pandemic")) %>% plot()
sleep2 <- glmer(
RSTLS_Sleep ~ timefactor*Pandemic*Age_sex + RSTLS_Sleepbaseline + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
(1|ID),
data = Tracking.data_long_2,
family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 3.59238 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
sleep2
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: RSTLS_Sleep ~ timefactor * Pandemic * Age_sex + RSTLS_Sleepbaseline +
## Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +
## SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +
## MoodDisord + Chronicconditions + (1 | ID)
## Data: Tracking.data_long_2
## AIC BIC logLik deviance df.resid
## 24481.72 24807.30 -12199.86 24399.72 20721
## Random effects:
## Groups Name Std.Dev.
## ID (Intercept) 0.8988
## Number of obs: 20762, groups: ID, 10407
## Fixed Effects:
## (Intercept)
## -1.671773
## timefactor2
## -0.281688
## PandemicFU2 data collected before COVID-19
## -0.155181
## Age_sexFemales 65+
## -0.226663
## Age_sexMales 45-64
## -0.510486
## Age_sexMales 65+
## -0.477572
## RSTLS_Sleepbaseline
## 0.911139
## EducationHigh School Diploma
## 0.135689
## EducationLess than High School Diploma
## 0.204534
## EducationSome College
## 0.132670
## EthnicityWhite
## 0.131442
## IncomeLevel>$150k
## 0.014420
## IncomeLevel$100-150k
## 0.077726
## IncomeLevel$20-50k
## -0.022403
## IncomeLevel$50-100k
## 0.037373
## BMI
## 0.003336
## CESD.10baseline
## 0.064369
## SmokingStatusFormer Smoker
## 0.031118
## SmokingStatusNever Smoked
## -0.077333
## SmokingStatusOccasional Smoker
## -0.088391
## RelationshipstatusMarried
## -0.053105
## RelationshipstatusSeparated
## -0.359240
## RelationshipstatusSingle
## -0.191453
## RelationshipstatusWidowed
## -0.167265
## LivingstatusAssisted Living
## 0.229839
## LivingstatusHouse
## 0.165971
## LivingstatusOther
## -0.064622
## AnxietyYes
## -0.121646
## MoodDisordYes
## -0.026124
## Chronicconditions
## 0.084981
## timefactor2:PandemicFU2 data collected before COVID-19
## 0.107955
## timefactor2:Age_sexFemales 65+
## 0.103330
## timefactor2:Age_sexMales 45-64
## 0.256567
## timefactor2:Age_sexMales 65+
## -0.159097
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## -0.076275
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## 0.365992
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## 0.140755
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## -0.134282
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## -0.374952
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## 0.245235
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 2 lme4 warnings
ggpredict(sleep2, c("Pandemic","timefactor","Age_sex"))
## # Predicted probabilities of RSTLS_Sleep
##
## # timefactor = 1
## # Age_sex = Females 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.32 | [0.25, 0.40]
## FU2 data collected before COVID-19 | 0.29 | [0.23, 0.36]
##
## # timefactor = 2
## # Age_sex = Females 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.27 | [0.20, 0.34]
## FU2 data collected before COVID-19 | 0.26 | [0.20, 0.33]
##
## # timefactor = 1
## # Age_sex = Females 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.28 | [0.21, 0.36]
## FU2 data collected before COVID-19 | 0.23 | [0.18, 0.30]
##
## # timefactor = 2
## # Age_sex = Females 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.24 | [0.18, 0.32]
## FU2 data collected before COVID-19 | 0.20 | [0.15, 0.26]
##
## # timefactor = 1
## # Age_sex = Males 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.22 | [0.17, 0.29]
## FU2 data collected before COVID-19 | 0.26 | [0.20, 0.33]
##
## # timefactor = 2
## # Age_sex = Males 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.22 | [0.17, 0.28]
## FU2 data collected before COVID-19 | 0.21 | [0.16, 0.27]
##
## # timefactor = 1
## # Age_sex = Males 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.23 | [0.17, 0.30]
## FU2 data collected before COVID-19 | 0.23 | [0.17, 0.30]
##
## # timefactor = 2
## # Age_sex = Males 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.16 | [0.12, 0.22]
## FU2 data collected before COVID-19 | 0.21 | [0.16, 0.28]
##
## Adjusted for:
## * RSTLS_Sleepbaseline = 0.33
## * Education = College Degree or Higher
## * Ethnicity = Other
## * IncomeLevel = <$20k
## * BMI = 27.53
## * CESD.10baseline = 4.88
## * SmokingStatus = Daily Smoker
## * Relationshipstatus = Divorced
## * Livingstatus = Apartment/Condo/Townhome
## * Anxiety = No
## * MoodDisord = No
## * Chronicconditions = 2.76
## * ID = 0 (population-level)
Mean differences and 95% CIs
sleep.test2 <- as.data.frame(ggpredict(sleep2, c("timefactor", "Pandemic", "Age_sex")))
#Females 45-64 years
mean.diff.Females.Young.3<-(subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
se.Females.Young.3<-(sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
UL.Females.Young.3<-mean.diff.Females.Young.3 + se.Females.Young.3*1.96
LL.Females.Young.3<-mean.diff.Females.Young.3 - se.Females.Young.3*1.96
mean.diff.Females.Young.4<-(subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
se.Females.Young.4<-(sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
UL.Females.Young.4<-mean.diff.Females.Young.4 + se.Females.Young.4*1.96
LL.Females.Young.4<-mean.diff.Females.Young.4 - se.Females.Young.4*1.96
#Females 65+ years
mean.diff.Females.Old.3<-(subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
se.Females.Old.3<-(sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
UL.Females.Old.3<-mean.diff.Females.Old.3 + se.Females.Old.3*1.96
LL.Females.Old.3<-mean.diff.Females.Old.3 - se.Females.Old.3*1.96
mean.diff.Females.Old.4<-(subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
se.Females.Old.4<-(sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
UL.Females.Old.4<-mean.diff.Females.Old.4 + se.Females.Old.4*1.96
LL.Females.Old.4<-mean.diff.Females.Old.4 - se.Females.Old.4*1.96
#Males 45-64 years
mean.diff.Males.Young.3<-(subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
se.Males.Young.3<-(sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
UL.Males.Young.3<-mean.diff.Males.Young.3 + se.Males.Young.3*1.96
LL.Males.Young.3<-mean.diff.Males.Young.3 - se.Males.Young.3*1.96
mean.diff.Males.Young.4<-(subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
se.Males.Young.4<-(sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
UL.Males.Young.4<-mean.diff.Males.Young.4 + se.Males.Young.4*1.96
LL.Males.Young.4<-mean.diff.Males.Young.4 - se.Males.Young.4*1.96
#Males 65+ years
mean.diff.Males.Old.3<-(subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
se.Males.Old.3<-(sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
UL.Males.Old.3<-mean.diff.Males.Old.3 + se.Males.Old.3*1.96
LL.Males.Old.3<-mean.diff.Males.Old.3 - se.Males.Old.3*1.96
mean.diff.Males.Old.4<-(subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
se.Males.Old.4<-(sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
UL.Males.Old.4<-mean.diff.Males.Old.4 + se.Males.Old.4*1.96
LL.Males.Old.4<-mean.diff.Males.Old.4 - se.Males.Old.4*1.96
#Mean differences and LL and UL
mean.diff.Females.Young.3
## [1] 0.0330407
UL.Females.Young.3
## [1] 0.3693358
LL.Females.Young.3
## [1] -0.3032544
mean.diff.Females.Young.4
## [1] 0.009115943
UL.Females.Young.4
## [1] 0.3465412
LL.Females.Young.4
## [1] -0.3283094
mean.diff.Females.Old.3
## [1] 0.04386866
UL.Females.Old.3
## [1] 0.4009714
LL.Females.Old.3
## [1] -0.3132341
mean.diff.Females.Old.4
## [1] 0.04418037
UL.Females.Old.4
## [1] 0.4030128
LL.Females.Old.4
## [1] -0.314652
mean.diff.Males.Young.3
## [1] -0.03871951
UL.Males.Young.3
## [1] 0.3049796
LL.Males.Young.3
## [1] -0.3824186
mean.diff.Males.Young.4
## [1] 0.009469295
UL.Males.Young.4
## [1] 0.3545126
LL.Males.Young.4
## [1] -0.335574
mean.diff.Males.Old.3
## [1] 0.002540603
UL.Males.Old.3
## [1] 0.3662314
LL.Males.Old.3
## [1] -0.3611502
mean.diff.Males.Old.4
## [1] -0.05110064
UL.Males.Old.4
## [1] 0.3168039
LL.Males.Old.4
## [1] -0.4190052
Z-scores
z.Females.Young.3 <- (subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
z.Females.Young.4 <- (subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
z.Females.Old.3 <- (subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
z.Females.Old.4 <- (subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
z.Males.Young.3 <- (subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
z.Males.Young.4 <- (subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
z.Males.Old.3 <- (subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sleep.test2,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sleep.test2,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
z.Males.Old.4 <- (subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sleep.test2,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sleep.test2,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
#z-scores
z.Females.Young.3
## [1] 0.1925683
z.Females.Young.4
## [1] 0.05295172
z.Males.Young.3
## [1] -0.2208043
z.Males.Young.4
## [1] 0.05378982
z.Females.Old.3
## [1] 0.2407782
z.Females.Old.4
## [1] 0.2413202
z.Males.Old.3
## [1] 0.0136918
z.Males.Old.4
## [1] -0.2722371
p-values for z-scores
2*pnorm(z.Females.Young.3, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.8472971
2*pnorm(z.Females.Young.4, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.9577704
2*pnorm(z.Males.Young.3, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 1.174755
2*pnorm(z.Males.Young.4, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.9571026
2*pnorm(z.Females.Old.3, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.809727
2*pnorm(z.Females.Old.4, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.8093069
2*pnorm(z.Males.Old.3, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.9890759
2*pnorm(z.Males.Old.4, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 1.21456
ggpredict(sleep2, c("timefactor","Pandemic","Age_sex")) %>% plot()
For this sensitivity analysis, we only include participants w/ data collected in 2019 onward (N=16,649)
startdate <- as.POSIXct("2019-01-01 00:00:00", tz = "EST")
truncated <- subset(Tracking.Adjusted_Final, timestamp>startdate)
Linear mixed model set-up
truncated.short<-truncated[c(1,4:12,14,15,18:21,146,147,98,115,132,102,119,136,106,123,140,110,127,144,13,44,75,22,53,84,23,54,85)]
truncated.short2<-rename(truncated.short, c("Age"="Age_0","Sex"="Sex_0","Ethnicity"="Ethnicity_0","Relationshipstatus"="Relationship_status_0",
"Education"="Education4_0", "IncomeLevel"="Income_Level_0", "Livingstatus"="Living_status_0",
"Alcohol"="Alcohol_0", "SmokingStatus"="Smoking_Status_0","Anxiety"="Anxiety_0","MoodDisord"="Mood_Disord_0",
"Chronicconditions"="Chronic_conditions_0", "BMI"="BMI_0","PASE_Sit_0"="PASE_Q1B_0","PASE_Sit_1"="PASE_Q1B_1","PASE_Sit_2"="PASE_Q1B_2"))
truncated.short2$PASE_TOTALbaseline <- truncated.short2$PASE_TOTAL_0
truncated.short3<-truncated.short2[c(1:18,40,19:39)]
truncated.short4<-truncated.short3[c(1:18,20,23,26,29,32,35,38,21:22,24:25,27:28,30:31,33:34,36:37,39:40)]
colnames(truncated.short3) <- (gsub("_2",".3",colnames(truncated.short3)))
colnames(truncated.short3) <- (gsub("_1",".2",colnames(truncated.short3)))
colnames(truncated.short3) <- (gsub("_0",".1",colnames(truncated.short3)))
colnames(truncated.short4) <- (gsub("_2",".2",colnames(truncated.short4)))
colnames(truncated.short4) <- (gsub("_1",".1",colnames(truncated.short4)))
colnames(truncated.short4) <- (gsub("_0","baseline",colnames(truncated.short4)))
truncated.data_long <- reshape(as.data.frame(truncated.short3),idvar="ID",varying=20:40,direction="long",sep=".") #reshape data into long format (3 timepoints)
truncated.data_long_2 <- reshape(as.data.frame(truncated.short4),idvar="ID",varying=26:39,direction="long",sep=".") #reshape data into long format (3 timepoints)
Indexed time as a categorical factor
#Treat time as a fixed effect
truncated.data_long$timefactor<-as.factor(truncated.data_long$time)
truncated.data_long_2$timefactor<-as.factor(truncated.data_long_2$time)
Age and Sex grouping
truncated.data_long$Age_sex<-NA
truncated.data_long$Age_sex[truncated.data_long$Age<=64 & truncated.data_long$Sex == "M"]<-"Males 45-64"
truncated.data_long$Age_sex[truncated.data_long$Age<=64 & truncated.data_long$Sex == "F"]<-"Females 45-64"
truncated.data_long$Age_sex[truncated.data_long$Age>64 & truncated.data_long$Sex == "M"]<-"Males 65+"
truncated.data_long$Age_sex[truncated.data_long$Age>64 & truncated.data_long$Sex == "F"]<-"Females 65+"
truncated.data_long_2$Age_sex<-NA
truncated.data_long_2$Age_sex[truncated.data_long_2$Age<=64 & truncated.data_long_2$Sex == "M"]<-"Males 45-64"
truncated.data_long_2$Age_sex[truncated.data_long_2$Age<=64 & truncated.data_long_2$Sex == "F"]<-"Females 45-64"
truncated.data_long_2$Age_sex[truncated.data_long_2$Age>64 & truncated.data_long_2$Sex == "M"]<-"Males 65+"
truncated.data_long_2$Age_sex[truncated.data_long_2$Age>64 & truncated.data_long_2$Sex == "F"]<-"Females 65+"
Truncated full sample (N= 16,649)
Baseline<-dput(names(truncated[c(5,4,14,12,6,7,8,9,10,11,15,18,19,20,13,28,30,26,23)]))
## c("Age_0", "Sex_0", "BMI_0", "Ethnicity_0", "Relationship_status_0",
## "Education4_0", "Income_Level_0", "Living_status_0", "Alcohol_0",
## "Smoking_Status_0", "CESD_10_0", "Anxiety_0", "Mood_Disord_0",
## "Pet_Owner_0", "PASE_TOTAL_0", "MAT_Score_0", "RVLT_Immediate_Score_0",
## "Animal_Fluency_Lenient_0", "RSTLS_Sleep_0")
Table1_truncated<-CreateTableOne(vars=Baseline, data=truncated)
print(Table1_truncated,contDigits=2,missing=TRUE,quote=TRUE)
## ""
## "" "Overall" "Missing"
## "n" " 9423" " "
## "Age_0 (mean (SD))" " 61.17 (10.22)" " 0.0"
## "Sex_0 = M (%)" " 4624 (49.1) " " 0.0"
## "BMI_0 (mean (SD))" " 27.48 (5.05)" " 0.5"
## "Ethnicity_0 = White (%)" " 9165 (97.3) " " 0.0"
## "Relationship_status_0 (%)" " " " 0.0"
## " Divorced" " 813 ( 8.6) " " "
## " Married" " 6900 (73.3) " " "
## " Separated" " 246 ( 2.6) " " "
## " Single" " 694 ( 7.4) " " "
## " Widowed" " 766 ( 8.1) " " "
## "Education4_0 (%)" " " " 0.0"
## " College Degree or Higher" " 6879 (73.0) " " "
## " High School Diploma" " 1213 (12.9) " " "
## " Less than High School Diploma" " 626 ( 6.6) " " "
## " Some College" " 705 ( 7.5) " " "
## "Income_Level_0 (%)" " " " 3.6"
## " <$20k" " 1449 (16.0) " " "
## " >$150k" " 380 ( 4.2) " " "
## " $100-150k" " 715 ( 7.9) " " "
## " $20-50k" " 3534 (38.9) " " "
## " $50-100k" " 3004 (33.1) " " "
## "Living_status_0 (%)" " " " 0.0"
## " Apartment/Condo/Townhome" " 1063 (11.3) " " "
## " Assisted Living" " 48 ( 0.5) " " "
## " House" " 8233 (87.4) " " "
## " Other" " 79 ( 0.8) " " "
## "Alcohol_0 (%)" " " " 3.0"
## " Non-drinker" " 929 (10.2) " " "
## " Occasional drinker" " 1439 (15.7) " " "
## " Regular drinker (at least once a month)" " 6773 (74.1) " " "
## "Smoking_Status_0 (%)" " " " 0.5"
## " Daily Smoker" " 628 ( 6.7) " " "
## " Former Smoker" " 5667 (60.4) " " "
## " Never Smoked" " 2927 (31.2) " " "
## " Occasional Smoker" " 156 ( 1.7) " " "
## "CESD_10_0 (mean (SD))" " 4.97 (4.37)" " 0.3"
## "Anxiety_0 = Yes (%)" " 598 ( 6.4) " " 0.1"
## "Mood_Disord_0 = Yes (%)" " 1266 (13.4) " " 0.1"
## "Pet_Owner_0 = Yes (%)" " 4526 (48.2) " " 0.4"
## "PASE_TOTAL_0 (mean (SD))" "172.78 (79.33)" "19.2"
## "MAT_Score_0 (mean (SD))" " 27.23 (9.14)" " 0.0"
## "RVLT_Immediate_Score_0 (mean (SD))" " 6.15 (2.25)" " 0.0"
## "Animal_Fluency_Lenient_0 (mean (SD))" " 22.08 (6.38)" " 0.0"
## "RSTLS_Sleep_0 (mean (SD))" " 0.33 (0.47)" " 0.2"
Final baseline sample stratified by whether FU2 data was collected before (N= 7132) or after (N= 6898) the start of the COVID-19 pandemic
Table1_truncated_stratify<-CreateTableOne(vars=Baseline, strata="Pandemic", data=truncated)
print(Table1_truncated_stratify,contDigits=2,missing=TRUE,quote=TRUE)
## "Stratified by Pandemic"
## "" "FU2 data collected after COVID-19"
## "n" " 5181"
## "Age_0 (mean (SD))" " 60.29 (10.54)"
## "Sex_0 = M (%)" " 2856 (55.1) "
## "BMI_0 (mean (SD))" " 27.52 (4.94)"
## "Ethnicity_0 = White (%)" " 5025 (97.0) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 408 ( 7.9) "
## " Married" " 3841 (74.2) "
## " Separated" " 150 ( 2.9) "
## " Single" " 383 ( 7.4) "
## " Widowed" " 397 ( 7.7) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 3605 (69.6) "
## " High School Diploma" " 776 (15.0) "
## " Less than High School Diploma" " 411 ( 7.9) "
## " Some College" " 389 ( 7.5) "
## "Income_Level_0 (%)" " "
## " <$20k" " 783 (15.6) "
## " >$150k" " 239 ( 4.8) "
## " $100-150k" " 426 ( 8.5) "
## " $20-50k" " 1874 (37.3) "
## " $50-100k" " 1700 (33.9) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 579 (11.2) "
## " Assisted Living" " 24 ( 0.5) "
## " House" " 4541 (87.6) "
## " Other" " 37 ( 0.7) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 512 (10.2) "
## " Occasional drinker" " 784 (15.6) "
## " Regular drinker (at least once a month)" " 3731 (74.2) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 361 ( 7.0) "
## " Former Smoker" " 3128 (60.6) "
## " Never Smoked" " 1579 (30.6) "
## " Occasional Smoker" " 90 ( 1.7) "
## "CESD_10_0 (mean (SD))" " 5.08 (4.47)"
## "Anxiety_0 = Yes (%)" " 335 ( 6.5) "
## "Mood_Disord_0 = Yes (%)" " 698 (13.5) "
## "Pet_Owner_0 = Yes (%)" " 2562 (49.6) "
## "PASE_TOTAL_0 (mean (SD))" "179.56 (81.39)"
## "MAT_Score_0 (mean (SD))" " 27.11 (9.20)"
## "RVLT_Immediate_Score_0 (mean (SD))" " 5.98 (2.20)"
## "Animal_Fluency_Lenient_0 (mean (SD))" " 21.92 (6.41)"
## "RSTLS_Sleep_0 (mean (SD))" " 0.33 (0.47)"
## "Stratified by Pandemic"
## "" "FU2 data collected before COVID-19"
## "n" " 4242"
## "Age_0 (mean (SD))" " 62.24 (9.72)"
## "Sex_0 = M (%)" " 1768 (41.7) "
## "BMI_0 (mean (SD))" " 27.43 (5.19)"
## "Ethnicity_0 = White (%)" " 4140 (97.6) "
## "Relationship_status_0 (%)" " "
## " Divorced" " 405 ( 9.6) "
## " Married" " 3059 (72.1) "
## " Separated" " 96 ( 2.3) "
## " Single" " 311 ( 7.3) "
## " Widowed" " 369 ( 8.7) "
## "Education4_0 (%)" " "
## " College Degree or Higher" " 3274 (77.2) "
## " High School Diploma" " 437 (10.3) "
## " Less than High School Diploma" " 215 ( 5.1) "
## " Some College" " 316 ( 7.4) "
## "Income_Level_0 (%)" " "
## " <$20k" " 666 (16.4) "
## " >$150k" " 141 ( 3.5) "
## " $100-150k" " 289 ( 7.1) "
## " $20-50k" " 1660 (40.9) "
## " $50-100k" " 1304 (32.1) "
## "Living_status_0 (%)" " "
## " Apartment/Condo/Townhome" " 484 (11.4) "
## " Assisted Living" " 24 ( 0.6) "
## " House" " 3692 (87.0) "
## " Other" " 42 ( 1.0) "
## "Alcohol_0 (%)" " "
## " Non-drinker" " 417 (10.1) "
## " Occasional drinker" " 655 (15.9) "
## " Regular drinker (at least once a month)" " 3042 (73.9) "
## "Smoking_Status_0 (%)" " "
## " Daily Smoker" " 267 ( 6.3) "
## " Former Smoker" " 2539 (60.2) "
## " Never Smoked" " 1348 (31.9) "
## " Occasional Smoker" " 66 ( 1.6) "
## "CESD_10_0 (mean (SD))" " 4.83 (4.24)"
## "Anxiety_0 = Yes (%)" " 263 ( 6.2) "
## "Mood_Disord_0 = Yes (%)" " 568 (13.4) "
## "Pet_Owner_0 = Yes (%)" " 1964 (46.5) "
## "PASE_TOTAL_0 (mean (SD))" "164.63 (76.01)"
## "MAT_Score_0 (mean (SD))" " 27.37 (9.06)"
## "RVLT_Immediate_Score_0 (mean (SD))" " 6.35 (2.28)"
## "Animal_Fluency_Lenient_0 (mean (SD))" " 22.28 (6.34)"
## "RSTLS_Sleep_0 (mean (SD))" " 0.33 (0.47)"
## "Stratified by Pandemic"
## "" "p" "test" "Missing"
## "n" "" "" " "
## "Age_0 (mean (SD))" "<0.001" "" " 0.0"
## "Sex_0 = M (%)" "<0.001" "" " 0.0"
## "BMI_0 (mean (SD))" " 0.378" "" " 0.5"
## "Ethnicity_0 = White (%)" " 0.083" "" " 0.0"
## "Relationship_status_0 (%)" " 0.004" "" " 0.0"
## " Divorced" "" "" " "
## " Married" "" "" " "
## " Separated" "" "" " "
## " Single" "" "" " "
## " Widowed" "" "" " "
## "Education4_0 (%)" "<0.001" "" " 0.0"
## " College Degree or Higher" "" "" " "
## " High School Diploma" "" "" " "
## " Less than High School Diploma" "" "" " "
## " Some College" "" "" " "
## "Income_Level_0 (%)" "<0.001" "" " 3.6"
## " <$20k" "" "" " "
## " >$150k" "" "" " "
## " $100-150k" "" "" " "
## " $20-50k" "" "" " "
## " $50-100k" "" "" " "
## "Living_status_0 (%)" " 0.421" "" " 0.0"
## " Apartment/Condo/Townhome" "" "" " "
## " Assisted Living" "" "" " "
## " House" "" "" " "
## " Other" "" "" " "
## "Alcohol_0 (%)" " 0.914" "" " 3.0"
## " Non-drinker" "" "" " "
## " Occasional drinker" "" "" " "
## " Regular drinker (at least once a month)" "" "" " "
## "Smoking_Status_0 (%)" " 0.331" "" " 0.5"
## " Daily Smoker" "" "" " "
## " Former Smoker" "" "" " "
## " Never Smoked" "" "" " "
## " Occasional Smoker" "" "" " "
## "CESD_10_0 (mean (SD))" " 0.006" "" " 0.3"
## "Anxiety_0 = Yes (%)" " 0.624" "" " 0.1"
## "Mood_Disord_0 = Yes (%)" " 0.940" "" " 0.1"
## "Pet_Owner_0 = Yes (%)" " 0.003" "" " 0.4"
## "PASE_TOTAL_0 (mean (SD))" "<0.001" "" "19.2"
## "MAT_Score_0 (mean (SD))" " 0.168" "" " 0.0"
## "RVLT_Immediate_Score_0 (mean (SD))" "<0.001" "" " 0.0"
## "Animal_Fluency_Lenient_0 (mean (SD))" " 0.006" "" " 0.0"
## "RSTLS_Sleep_0 (mean (SD))" " 0.804" "" " 0.2"
All models use normalized cognitive scores.Each model is adjusted for baseline age, sex, education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, and baseline PASE score
modelRVLT_imm_8_trun<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= truncated.data_long)
summary(modelRVLT_imm_8_trun)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: truncated.data_long
##
## REML criterion at convergence: 109191.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6122 -0.5799 -0.0477 0.5194 4.5803
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.212 2.283
## Residual 8.380 2.895
## Number of obs: 20522, groups: ID, 6987
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 7.893e+00 4.680e-01
## timefactor2 7.000e-01 6.657e-02
## timefactor3 1.013e+00 6.725e-02
## PandemicFU2 data collected before COVID-19 4.511e-01 8.995e-02
## Age 1.556e-02 4.403e-03
## SexM -7.664e-01 7.666e-02
## EducationHigh School Diploma 1.917e-01 1.076e-01
## EducationLess than High School Diploma 6.156e-01 1.525e-01
## EducationSome College 2.297e-01 1.337e-01
## EthnicityWhite 1.219e+00 2.132e-01
## IncomeLevel>$150k 8.362e-01 1.968e-01
## IncomeLevel$100-150k 4.443e-01 1.599e-01
## IncomeLevel$20-50k 8.045e-02 1.078e-01
## IncomeLevel$50-100k 5.144e-01 1.159e-01
## BMI -1.805e-02 7.266e-03
## CESD.20.1 -3.680e-02 8.659e-03
## SmokingStatusFormer Smoker 3.629e-01 1.467e-01
## SmokingStatusNever Smoked 5.008e-01 1.526e-01
## SmokingStatusOccasional Smoker 4.226e-01 2.895e-01
## RelationshipstatusMarried 1.075e-01 1.260e-01
## RelationshipstatusSeparated -1.496e-01 2.440e-01
## RelationshipstatusSingle -4.828e-02 1.709e-01
## RelationshipstatusWidowed -6.016e-02 1.750e-01
## LivingstatusAssisted Living -3.970e-01 5.315e-01
## LivingstatusHouse 2.161e-02 1.150e-01
## LivingstatusOther -5.238e-01 4.278e-01
## AnxietyYes -4.922e-02 1.507e-01
## MoodDisordYes -8.521e-02 1.087e-01
## Chronicconditions -3.093e-02 1.776e-02
## PASE_TOTALbaseline 2.891e-03 5.101e-04
## timefactor2:PandemicFU2 data collected before COVID-19 -1.758e-01 9.916e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -2.584e-01 9.967e-02
## df t value
## (Intercept) 7.013e+03 16.865
## timefactor2 1.358e+04 10.515
## timefactor3 1.366e+04 15.069
## PandemicFU2 data collected before COVID-19 1.554e+04 5.015
## Age 6.928e+03 3.535
## SexM 6.927e+03 -9.998
## EducationHigh School Diploma 6.932e+03 1.781
## EducationLess than High School Diploma 6.992e+03 4.037
## EducationSome College 6.898e+03 1.718
## EthnicityWhite 6.898e+03 5.719
## IncomeLevel>$150k 6.935e+03 4.249
## IncomeLevel$100-150k 6.909e+03 2.779
## IncomeLevel$20-50k 6.933e+03 0.747
## IncomeLevel$50-100k 6.932e+03 4.439
## BMI 6.916e+03 -2.485
## CESD.20.1 6.925e+03 -4.250
## SmokingStatusFormer Smoker 6.936e+03 2.474
## SmokingStatusNever Smoked 6.936e+03 3.282
## SmokingStatusOccasional Smoker 6.862e+03 1.460
## RelationshipstatusMarried 6.935e+03 0.853
## RelationshipstatusSeparated 6.965e+03 -0.613
## RelationshipstatusSingle 6.935e+03 -0.283
## RelationshipstatusWidowed 6.939e+03 -0.344
## LivingstatusAssisted Living 6.931e+03 -0.747
## LivingstatusHouse 6.938e+03 0.188
## LivingstatusOther 6.855e+03 -1.224
## AnxietyYes 6.912e+03 -0.327
## MoodDisordYes 6.924e+03 -0.784
## Chronicconditions 6.920e+03 -1.741
## PASE_TOTALbaseline 6.920e+03 5.668
## timefactor2:PandemicFU2 data collected before COVID-19 1.358e+04 -1.773
## timefactor3:PandemicFU2 data collected before COVID-19 1.362e+04 -2.593
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## timefactor3 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 5.36e-07 ***
## Age 0.000411 ***
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.074885 .
## EducationLess than High School Diploma 5.47e-05 ***
## EducationSome College 0.085816 .
## EthnicityWhite 1.12e-08 ***
## IncomeLevel>$150k 2.18e-05 ***
## IncomeLevel$100-150k 0.005468 **
## IncomeLevel$20-50k 0.455385
## IncomeLevel$50-100k 9.18e-06 ***
## BMI 0.012995 *
## CESD.20.1 2.16e-05 ***
## SmokingStatusFormer Smoker 0.013388 *
## SmokingStatusNever Smoked 0.001037 **
## SmokingStatusOccasional Smoker 0.144423
## RelationshipstatusMarried 0.393545
## RelationshipstatusSeparated 0.539819
## RelationshipstatusSingle 0.777566
## RelationshipstatusWidowed 0.731066
## LivingstatusAssisted Living 0.455120
## LivingstatusHouse 0.850865
## LivingstatusOther 0.220882
## AnxietyYes 0.743904
## MoodDisordYes 0.432980
## Chronicconditions 0.081681 .
## PASE_TOTALbaseline 1.50e-08 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.076316 .
## timefactor3:PandemicFU2 data collected before COVID-19 0.009525 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_imm_8_trun, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.453406 0.2346745 Inf 8.993453 9.91336
## FU2 data collected before COVID-19 9.904496 0.2381181 Inf 9.437793 10.37120
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.153375 0.2347814 Inf 9.693212 10.61354
## FU2 data collected before COVID-19 10.428696 0.2382731 Inf 9.961689 10.89570
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.466794 0.2349867 Inf 10.006228 10.92736
## FU2 data collected before COVID-19 10.659448 0.2382748 Inf 10.192438 11.12646
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_8_trun, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4510900 0.08994764 Inf -5.015 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2753214 0.09072466 Inf -3.035 0.0024
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1926541 0.09130995 Inf -2.110 0.0349
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_imm_8_trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4510900 0.08994764 Inf -0.6273842 -0.27479591
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2753214 0.09072466 Inf -0.4531384 -0.09750432
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1926541 0.09130995 Inf -0.3716183 -0.01368988
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTimmediate_lsmeans_8trun <- summary(lsmeans(modelRVLT_imm_8_trun, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTimmediate_lsmeans_8trun$Time<-NA
RVLTimmediate_lsmeans_8trun$Time[RVLTimmediate_lsmeans_8trun$timefactor==1]<-"Baseline"
RVLTimmediate_lsmeans_8trun$Time[RVLTimmediate_lsmeans_8trun$timefactor==2]<-"Follow-up 1"
RVLTimmediate_lsmeans_8trun$Time[RVLTimmediate_lsmeans_8trun$timefactor==3]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_8trun, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "RVLT Immediate Score", title = "RVLT Immediate Score from Baseline to FU2 by Pandemic status") +
theme_bw()
modelRVLT_del_8trun<- lmer(RVLT_Delayed_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= truncated.data_long)
summary(modelRVLT_del_8trun)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: truncated.data_long
##
## REML criterion at convergence: 107532.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8255 -0.5652 -0.0365 0.5143 5.0735
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.571 2.360
## Residual 7.698 2.775
## Number of obs: 20402, groups: ID, 6987
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 7.850e+00 4.725e-01
## timefactor2 5.563e-01 6.405e-02
## timefactor3 1.143e+00 6.467e-02
## PandemicFU2 data collected before COVID-19 3.733e-01 8.893e-02
## Age 2.642e-02 4.444e-03
## SexM -6.548e-01 7.735e-02
## EducationHigh School Diploma 3.673e-01 1.086e-01
## EducationLess than High School Diploma 5.761e-01 1.541e-01
## EducationSome College 3.710e-01 1.348e-01
## EthnicityWhite 1.110e+00 2.158e-01
## IncomeLevel>$150k 6.652e-01 1.985e-01
## IncomeLevel$100-150k 5.232e-01 1.613e-01
## IncomeLevel$20-50k 2.135e-01 1.088e-01
## IncomeLevel$50-100k 5.715e-01 1.169e-01
## BMI -1.843e-02 7.330e-03
## CESD.20.1 -3.674e-02 8.742e-03
## SmokingStatusFormer Smoker 9.877e-02 1.480e-01
## SmokingStatusNever Smoked 3.429e-01 1.539e-01
## SmokingStatusOccasional Smoker 3.115e-01 2.924e-01
## RelationshipstatusMarried -4.107e-02 1.272e-01
## RelationshipstatusSeparated -1.311e-01 2.463e-01
## RelationshipstatusSingle -2.088e-01 1.724e-01
## RelationshipstatusWidowed -1.076e-01 1.767e-01
## LivingstatusAssisted Living -1.210e+00 5.368e-01
## LivingstatusHouse -1.293e-03 1.160e-01
## LivingstatusOther -2.917e-01 4.325e-01
## AnxietyYes -2.939e-02 1.521e-01
## MoodDisordYes -1.189e-01 1.096e-01
## Chronicconditions -4.024e-02 1.792e-02
## PASE_TOTALbaseline 3.072e-03 5.148e-04
## timefactor2:PandemicFU2 data collected before COVID-19 -6.747e-03 9.535e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -2.638e-01 9.580e-02
## df t value
## (Intercept) 7.027e+03 16.614
## timefactor2 1.350e+04 8.686
## timefactor3 1.356e+04 17.682
## PandemicFU2 data collected before COVID-19 1.482e+04 4.198
## Age 6.939e+03 5.946
## SexM 6.929e+03 -8.466
## EducationHigh School Diploma 6.941e+03 3.383
## EducationLess than High School Diploma 7.028e+03 3.738
## EducationSome College 6.891e+03 2.752
## EthnicityWhite 6.974e+03 5.146
## IncomeLevel>$150k 6.931e+03 3.351
## IncomeLevel$100-150k 6.913e+03 3.244
## IncomeLevel$20-50k 6.942e+03 1.963
## IncomeLevel$50-100k 6.935e+03 4.887
## BMI 6.912e+03 -2.514
## CESD.20.1 6.941e+03 -4.203
## SmokingStatusFormer Smoker 6.927e+03 0.668
## SmokingStatusNever Smoked 6.928e+03 2.228
## SmokingStatusOccasional Smoker 6.887e+03 1.065
## RelationshipstatusMarried 6.946e+03 -0.323
## RelationshipstatusSeparated 6.982e+03 -0.532
## RelationshipstatusSingle 6.934e+03 -1.211
## RelationshipstatusWidowed 6.950e+03 -0.609
## LivingstatusAssisted Living 6.962e+03 -2.254
## LivingstatusHouse 6.952e+03 -0.011
## LivingstatusOther 6.903e+03 -0.674
## AnxietyYes 6.920e+03 -0.193
## MoodDisordYes 6.920e+03 -1.085
## Chronicconditions 6.923e+03 -2.245
## PASE_TOTALbaseline 6.930e+03 5.967
## timefactor2:PandemicFU2 data collected before COVID-19 1.349e+04 -0.071
## timefactor3:PandemicFU2 data collected before COVID-19 1.352e+04 -2.754
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## timefactor3 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 2.71e-05 ***
## Age 2.89e-09 ***
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.000722 ***
## EducationLess than High School Diploma 0.000187 ***
## EducationSome College 0.005941 **
## EthnicityWhite 2.73e-07 ***
## IncomeLevel>$150k 0.000810 ***
## IncomeLevel$100-150k 0.001186 **
## IncomeLevel$20-50k 0.049638 *
## IncomeLevel$50-100k 1.04e-06 ***
## BMI 0.011947 *
## CESD.20.1 2.67e-05 ***
## SmokingStatusFormer Smoker 0.504433
## SmokingStatusNever Smoked 0.025898 *
## SmokingStatusOccasional Smoker 0.286748
## RelationshipstatusMarried 0.746819
## RelationshipstatusSeparated 0.594524
## RelationshipstatusSingle 0.225989
## RelationshipstatusWidowed 0.542607
## LivingstatusAssisted Living 0.024220 *
## LivingstatusHouse 0.991112
## LivingstatusOther 0.500090
## AnxietyYes 0.846740
## MoodDisordYes 0.278125
## Chronicconditions 0.024786 *
## PASE_TOTALbaseline 2.54e-09 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.943589
## timefactor3:PandemicFU2 data collected before COVID-19 0.005894 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_del_8trun, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.79743 0.2367786 Inf 9.333353 10.26151
## FU2 data collected before COVID-19 10.17072 0.2401909 Inf 9.699954 10.64148
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.35377 0.2369809 Inf 9.889297 10.81824
## FU2 data collected before COVID-19 10.72031 0.2404243 Inf 10.249090 11.19154
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.94086 0.2371645 Inf 10.476023 11.40569
## FU2 data collected before COVID-19 11.05030 0.2404106 Inf 10.579107 11.52150
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_8trun, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3732888 0.08892997 Inf -4.198 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3665420 0.08996807 Inf -4.074 <.0001
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1094461 0.09049012 Inf -1.209 0.2265
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_del_8trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3732888 0.08892997 Inf -0.5475884 -0.19898931
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3665420 0.08996807 Inf -0.5428762 -0.19020787
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1094461 0.09049012 Inf -0.2868035 0.06791125
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTdelayed_lsmeans_8trun <- summary(lsmeans(modelRVLT_del_8trun, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTdelayed_lsmeans_8trun$Time<-NA
RVLTdelayed_lsmeans_8trun$Time[RVLTdelayed_lsmeans_8trun$timefactor==1]<-"Baseline"
RVLTdelayed_lsmeans_8trun$Time[RVLTdelayed_lsmeans_8trun$timefactor==2]<-"Follow-up 1"
RVLTdelayed_lsmeans_8trun$Time[RVLTdelayed_lsmeans_8trun$timefactor==3]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_8trun, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "RVLT Delayed Score", title = "RVLT Delayed Score from Baseline to FU2 by Pandemic status") +
theme_bw()
modelMAT_8trun<- lmer(MAT_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= truncated.data_long)
summary(modelMAT_8trun)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: truncated.data_long
##
## REML criterion at convergence: 103602.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9324 -0.4800 -0.0217 0.4226 4.7815
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.907 2.215
## Residual 8.093 2.845
## Number of obs: 19603, groups: ID, 6987
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 9.872e+00 4.618e-01
## timefactor2 1.090e+00 6.717e-02
## timefactor3 -2.731e-01 6.761e-02
## PandemicFU2 data collected before COVID-19 -3.908e-03 8.799e-02
## Age -2.577e-03 4.348e-03
## SexM -1.096e+00 7.553e-02
## EducationHigh School Diploma 5.325e-03 1.061e-01
## EducationLess than High School Diploma -2.912e-02 1.513e-01
## EducationSome College 7.804e-02 1.315e-01
## EthnicityWhite 1.380e+00 2.110e-01
## IncomeLevel>$150k 7.419e-01 1.938e-01
## IncomeLevel$100-150k 8.372e-01 1.574e-01
## IncomeLevel$20-50k 3.364e-01 1.063e-01
## IncomeLevel$50-100k 6.873e-01 1.143e-01
## BMI -2.270e-02 7.150e-03
## CESD.20.1 -4.726e-02 8.533e-03
## SmokingStatusFormer Smoker 1.741e-01 1.443e-01
## SmokingStatusNever Smoked 1.158e-01 1.501e-01
## SmokingStatusOccasional Smoker 1.618e-01 2.852e-01
## RelationshipstatusMarried 1.802e-01 1.243e-01
## RelationshipstatusSeparated -1.733e-01 2.399e-01
## RelationshipstatusSingle 2.984e-01 1.685e-01
## RelationshipstatusWidowed -1.861e-01 1.730e-01
## LivingstatusAssisted Living -4.556e-01 5.245e-01
## LivingstatusHouse -1.610e-01 1.135e-01
## LivingstatusOther -2.618e-01 4.236e-01
## AnxietyYes 1.170e-01 1.483e-01
## MoodDisordYes 3.088e-01 1.069e-01
## Chronicconditions -6.526e-02 1.752e-02
## PASE_TOTALbaseline -7.932e-04 5.020e-04
## timefactor2:PandemicFU2 data collected before COVID-19 -2.752e-01 1.001e-01
## timefactor3:PandemicFU2 data collected before COVID-19 8.568e-02 9.988e-02
## df t value
## (Intercept) 6.993e+03 21.376
## timefactor2 1.291e+04 16.221
## timefactor3 1.295e+04 -4.040
## PandemicFU2 data collected before COVID-19 1.511e+04 -0.044
## Age 6.928e+03 -0.593
## SexM 6.874e+03 -14.507
## EducationHigh School Diploma 6.893e+03 0.050
## EducationLess than High School Diploma 7.072e+03 -0.192
## EducationSome College 6.815e+03 0.594
## EthnicityWhite 6.951e+03 6.541
## IncomeLevel>$150k 6.864e+03 3.829
## IncomeLevel$100-150k 6.841e+03 5.321
## IncomeLevel$20-50k 6.913e+03 3.163
## IncomeLevel$50-100k 6.895e+03 6.015
## BMI 6.832e+03 -3.174
## CESD.20.1 6.871e+03 -5.539
## SmokingStatusFormer Smoker 6.836e+03 1.207
## SmokingStatusNever Smoked 6.837e+03 0.772
## SmokingStatusOccasional Smoker 6.790e+03 0.567
## RelationshipstatusMarried 6.910e+03 1.450
## RelationshipstatusSeparated 6.875e+03 -0.722
## RelationshipstatusSingle 6.892e+03 1.771
## RelationshipstatusWidowed 6.955e+03 -1.076
## LivingstatusAssisted Living 6.888e+03 -0.869
## LivingstatusHouse 6.926e+03 -1.419
## LivingstatusOther 6.912e+03 -0.618
## AnxietyYes 6.843e+03 0.789
## MoodDisordYes 6.836e+03 2.889
## Chronicconditions 6.892e+03 -3.724
## PASE_TOTALbaseline 6.841e+03 -1.580
## timefactor2:PandemicFU2 data collected before COVID-19 1.290e+04 -2.750
## timefactor3:PandemicFU2 data collected before COVID-19 1.288e+04 0.858
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## timefactor3 5.37e-05 ***
## PandemicFU2 data collected before COVID-19 0.964574
## Age 0.553493
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.959980
## EducationLess than High School Diploma 0.847361
## EducationSome College 0.552816
## EthnicityWhite 6.54e-11 ***
## IncomeLevel>$150k 0.000130 ***
## IncomeLevel$100-150k 1.07e-07 ***
## IncomeLevel$20-50k 0.001566 **
## IncomeLevel$50-100k 1.90e-09 ***
## BMI 0.001508 **
## CESD.20.1 3.16e-08 ***
## SmokingStatusFormer Smoker 0.227651
## SmokingStatusNever Smoked 0.440239
## SmokingStatusOccasional Smoker 0.570396
## RelationshipstatusMarried 0.147059
## RelationshipstatusSeparated 0.470146
## RelationshipstatusSingle 0.076539 .
## RelationshipstatusWidowed 0.281992
## LivingstatusAssisted Living 0.385105
## LivingstatusHouse 0.156030
## LivingstatusOther 0.536627
## AnxietyYes 0.430189
## MoodDisordYes 0.003881 **
## Chronicconditions 0.000197 ***
## PASE_TOTALbaseline 0.114129
## timefactor2:PandemicFU2 data collected before COVID-19 0.005972 **
## timefactor3:PandemicFU2 data collected before COVID-19 0.391014
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelMAT_8trun, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.367255 0.2316145 Inf 8.913299 9.821211
## FU2 data collected before COVID-19 9.363347 0.2350058 Inf 8.902744 9.823950
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.456874 0.2323708 Inf 10.001436 10.912312
## FU2 data collected before COVID-19 10.177789 0.2357663 Inf 9.715696 10.639883
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.094120 0.2321972 Inf 8.639022 9.549219
## FU2 data collected before COVID-19 9.175889 0.2356319 Inf 8.714059 9.637719
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_8trun, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.00390799 0.08798809 Inf 0.044 0.9646
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.27908487 0.09165936 Inf 3.045 0.0023
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.08176819 0.09144812 Inf -0.894 0.3712
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelMAT_8trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.00390799 0.08798809 Inf -0.16854550 0.1763615
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.27908487 0.09165936 Inf 0.09943582 0.4587339
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.08176819 0.09144812 Inf -0.26100321 0.0974668
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
MAT_lsmeans_8trun <- summary(lsmeans(modelMAT_8trun, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
MAT_lsmeans_8trun$Time<-NA
MAT_lsmeans_8trun$Time[MAT_lsmeans_8trun$timefactor==1]<-"Baseline"
MAT_lsmeans_8trun$Time[MAT_lsmeans_8trun$timefactor==2]<-"Follow-up 1"
MAT_lsmeans_8trun$Time[MAT_lsmeans_8trun$timefactor==3]<-"Follow-up 2"
ggplot(MAT_lsmeans_8trun, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "MAT Score", title = "Mental Alteration Test Score from Baseline to FU2 by Pandemic status") +
theme_bw()
modelAnimals_8trun<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= truncated.data_long)
summary(modelAnimals_8trun)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: truncated.data_long
##
## REML criterion at convergence: 96464.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3556 -0.5650 -0.0117 0.5536 4.4684
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.871 2.207
## Residual 3.648 1.910
## Number of obs: 20626, groups: ID, 6987
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 8.979e+00 4.065e-01
## timefactor2 -1.427e-01 4.386e-02
## timefactor3 -5.172e-02 4.435e-02
## PandemicFU2 data collected before COVID-19 2.025e-01 7.142e-02
## Age -2.083e-03 3.828e-03
## SexM -2.582e-01 6.667e-02
## EducationHigh School Diploma 3.276e-01 9.359e-02
## EducationLess than High School Diploma 3.278e-01 1.324e-01
## EducationSome College 2.877e-01 1.163e-01
## EthnicityWhite 1.379e+00 1.855e-01
## IncomeLevel>$150k 5.279e-01 1.711e-01
## IncomeLevel$100-150k 4.182e-01 1.391e-01
## IncomeLevel$20-50k 5.155e-02 9.370e-02
## IncomeLevel$50-100k 2.568e-01 1.008e-01
## BMI -1.644e-02 6.320e-03
## CESD.20.1 -3.514e-02 7.533e-03
## SmokingStatusFormer Smoker 2.922e-01 1.276e-01
## SmokingStatusNever Smoked 2.416e-01 1.327e-01
## SmokingStatusOccasional Smoker 4.370e-01 2.522e-01
## RelationshipstatusMarried -2.504e-01 1.096e-01
## RelationshipstatusSeparated -7.852e-02 2.120e-01
## RelationshipstatusSingle -1.519e-01 1.486e-01
## RelationshipstatusWidowed -2.843e-01 1.521e-01
## LivingstatusAssisted Living -2.298e-01 4.625e-01
## LivingstatusHouse 3.577e-01 9.996e-02
## LivingstatusOther 2.292e-01 3.724e-01
## AnxietyYes -4.920e-02 1.311e-01
## MoodDisordYes 2.641e-01 9.452e-02
## Chronicconditions -7.964e-03 1.545e-02
## PASE_TOTALbaseline 1.159e-03 4.436e-04
## timefactor2:PandemicFU2 data collected before COVID-19 8.875e-03 6.527e-02
## timefactor3:PandemicFU2 data collected before COVID-19 4.496e-02 6.564e-02
## df t value
## (Intercept) 7.007e+03 22.087
## timefactor2 1.367e+04 -3.255
## timefactor3 1.372e+04 -1.166
## PandemicFU2 data collected before COVID-19 1.227e+04 2.836
## Age 6.949e+03 -0.544
## SexM 6.954e+03 -3.872
## EducationHigh School Diploma 6.962e+03 3.501
## EducationLess than High School Diploma 6.982e+03 2.475
## EducationSome College 6.938e+03 2.473
## EthnicityWhite 6.943e+03 7.431
## IncomeLevel>$150k 6.955e+03 3.085
## IncomeLevel$100-150k 6.948e+03 3.007
## IncomeLevel$20-50k 6.954e+03 0.550
## IncomeLevel$50-100k 6.955e+03 2.548
## BMI 6.945e+03 -2.601
## CESD.20.1 6.959e+03 -4.665
## SmokingStatusFormer Smoker 6.954e+03 2.291
## SmokingStatusNever Smoked 6.954e+03 1.821
## SmokingStatusOccasional Smoker 6.924e+03 1.733
## RelationshipstatusMarried 6.969e+03 -2.284
## RelationshipstatusSeparated 6.974e+03 -0.370
## RelationshipstatusSingle 6.965e+03 -1.022
## RelationshipstatusWidowed 6.955e+03 -1.869
## LivingstatusAssisted Living 6.974e+03 -0.497
## LivingstatusHouse 6.964e+03 3.578
## LivingstatusOther 6.901e+03 0.615
## AnxietyYes 6.951e+03 -0.375
## MoodDisordYes 6.957e+03 2.794
## Chronicconditions 6.945e+03 -0.516
## PASE_TOTALbaseline 6.949e+03 2.612
## timefactor2:PandemicFU2 data collected before COVID-19 1.366e+04 0.136
## timefactor3:PandemicFU2 data collected before COVID-19 1.369e+04 0.685
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 0.001138 **
## timefactor3 0.243588
## PandemicFU2 data collected before COVID-19 0.004580 **
## Age 0.586412
## SexM 0.000109 ***
## EducationHigh School Diploma 0.000467 ***
## EducationLess than High School Diploma 0.013341 *
## EducationSome College 0.013404 *
## EthnicityWhite 1.20e-13 ***
## IncomeLevel>$150k 0.002041 **
## IncomeLevel$100-150k 0.002651 **
## IncomeLevel$20-50k 0.582215
## IncomeLevel$50-100k 0.010848 *
## BMI 0.009302 **
## CESD.20.1 3.15e-06 ***
## SmokingStatusFormer Smoker 0.021996 *
## SmokingStatusNever Smoked 0.068609 .
## SmokingStatusOccasional Smoker 0.083147 .
## RelationshipstatusMarried 0.022373 *
## RelationshipstatusSeparated 0.711183
## RelationshipstatusSingle 0.306991
## RelationshipstatusWidowed 0.061663 .
## LivingstatusAssisted Living 0.619357
## LivingstatusHouse 0.000349 ***
## LivingstatusOther 0.538362
## AnxietyYes 0.707469
## MoodDisordYes 0.005215 **
## Chronicconditions 0.606167
## PASE_TOTALbaseline 0.009023 **
## timefactor2:PandemicFU2 data collected before COVID-19 0.891832
## timefactor3:PandemicFU2 data collected before COVID-19 0.493368
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 32 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelAnimals_8trun, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.750112 0.2031309 Inf 9.351982 10.14824
## FU2 data collected before COVID-19 9.952642 0.2058940 Inf 9.549097 10.35619
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.607374 0.2031899 Inf 9.209129 10.00562
## FU2 data collected before COVID-19 9.818779 0.2059189 Inf 9.415185 10.22237
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.698391 0.2032711 Inf 9.299986 10.09680
## FU2 data collected before COVID-19 9.945882 0.2059684 Inf 9.542192 10.34957
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_8trun, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2025299 0.07142133 Inf -2.836 0.0046
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2114053 0.07170107 Inf -2.948 0.0032
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2474918 0.07206497 Inf -3.434 0.0006
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelAnimals_8trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2025299 0.07142133 Inf -0.3425131 -0.06254665
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2114053 0.07170107 Inf -0.3519369 -0.07087383
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2474918 0.07206497 Inf -0.3887365 -0.10624706
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
Animals_lsmeans_8trun <- summary(lsmeans(modelAnimals_8trun, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
Animals_lsmeans_8trun$Time<-NA
Animals_lsmeans_8trun$Time[Animals_lsmeans_8trun$timefactor==1]<-"Baseline"
Animals_lsmeans_8trun$Time[Animals_lsmeans_8trun$timefactor==2]<-"Follow-up 1"
Animals_lsmeans_8trun$Time[Animals_lsmeans_8trun$timefactor==3]<-"Follow-up 2"
ggplot(Animals_lsmeans_8trun, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "Animal Fluency (words)", title = "Animal Fluency Score from Baseline to FU2 by Pandemic status") +
theme_bw()
All models use normalized cognitive scores. Each model is adjusted for baseline age, sex, education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, baseline PASE score, and baseline cognitive performance
modelRVLT_imm_adj10trun<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
(1|ID), data= truncated.data_long_2)
summary(modelRVLT_imm_adj10trun)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## RVLT_Immediate_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
## (1 | ID)
## Data: truncated.data_long_2
##
## REML criterion at convergence: 70686.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6998 -0.5714 -0.0379 0.5273 3.8358
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 3.951 1.988
## Residual 7.521 2.743
## Number of obs: 13535, groups: ID, 6973
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 7.310e+00 4.650e-01
## timefactor2 3.109e-01 6.425e-02
## PandemicFU2 data collected before COVID-19 1.741e-01 8.368e-02
## Age -1.625e-02 4.367e-03
## SexM -5.030e-01 7.602e-02
## EducationHigh School Diploma 2.889e-01 1.063e-01
## EducationLess than High School Diploma 4.337e-01 1.512e-01
## EducationSome College 1.433e-01 1.319e-01
## EthnicityWhite 6.392e-01 2.106e-01
## IncomeLevel>$150k 3.879e-01 1.947e-01
## IncomeLevel$100-150k 2.799e-01 1.578e-01
## IncomeLevel$20-50k 1.605e-01 1.064e-01
## IncomeLevel$50-100k 4.062e-01 1.145e-01
## BMI -1.609e-02 7.173e-03
## CESD.10baseline -2.310e-02 8.556e-03
## SmokingStatusFormer Smoker 2.334e-01 1.450e-01
## SmokingStatusNever Smoked 3.461e-01 1.508e-01
## SmokingStatusOccasional Smoker 4.729e-02 2.850e-01
## RelationshipstatusMarried 3.348e-01 1.245e-01
## RelationshipstatusSeparated 3.097e-01 2.415e-01
## RelationshipstatusSingle 2.316e-01 1.689e-01
## RelationshipstatusWidowed 3.257e-02 1.729e-01
## LivingstatusAssisted Living -9.343e-01 5.249e-01
## LivingstatusHouse -3.798e-02 1.136e-01
## LivingstatusOther -5.045e-01 4.209e-01
## AnxietyYes 5.017e-02 1.486e-01
## MoodDisordYes -1.114e-01 1.073e-01
## Chronicconditions -2.421e-02 1.754e-02
## PASE_TOTALbaseline 2.198e-03 5.041e-04
## RVLT_Immediate_Normedbaseline 3.554e-01 8.988e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -7.979e-02 9.517e-02
## df t value
## (Intercept) 6.954e+03 15.720
## timefactor2 6.830e+03 4.839
## PandemicFU2 data collected before COVID-19 1.203e+04 2.081
## Age 6.915e+03 -3.721
## SexM 6.911e+03 -6.617
## EducationHigh School Diploma 6.917e+03 2.718
## EducationLess than High School Diploma 7.040e+03 2.869
## EducationSome College 6.839e+03 1.086
## EthnicityWhite 6.840e+03 3.035
## IncomeLevel>$150k 6.916e+03 1.992
## IncomeLevel$100-150k 6.873e+03 1.774
## IncomeLevel$20-50k 6.924e+03 1.508
## IncomeLevel$50-100k 6.921e+03 3.547
## BMI 6.887e+03 -2.244
## CESD.10baseline 6.911e+03 -2.699
## SmokingStatusFormer Smoker 6.937e+03 1.610
## SmokingStatusNever Smoked 6.937e+03 2.295
## SmokingStatusOccasional Smoker 6.807e+03 0.166
## RelationshipstatusMarried 6.926e+03 2.690
## RelationshipstatusSeparated 6.989e+03 1.282
## RelationshipstatusSingle 6.926e+03 1.371
## RelationshipstatusWidowed 6.931e+03 0.188
## LivingstatusAssisted Living 6.929e+03 -1.780
## LivingstatusHouse 6.923e+03 -0.334
## LivingstatusOther 6.794e+03 -1.199
## AnxietyYes 6.894e+03 0.338
## MoodDisordYes 6.909e+03 -1.038
## Chronicconditions 6.902e+03 -1.381
## PASE_TOTALbaseline 6.898e+03 4.360
## RVLT_Immediate_Normedbaseline 6.927e+03 39.546
## timefactor2:PandemicFU2 data collected before COVID-19 6.778e+03 -0.838
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 1.33e-06 ***
## PandemicFU2 data collected before COVID-19 0.037474 *
## Age 0.000200 ***
## SexM 3.93e-11 ***
## EducationHigh School Diploma 0.006591 **
## EducationLess than High School Diploma 0.004131 **
## EducationSome College 0.277388
## EthnicityWhite 0.002417 **
## IncomeLevel>$150k 0.046385 *
## IncomeLevel$100-150k 0.076170 .
## IncomeLevel$20-50k 0.131549
## IncomeLevel$50-100k 0.000392 ***
## BMI 0.024890 *
## CESD.10baseline 0.006964 **
## SmokingStatusFormer Smoker 0.107355
## SmokingStatusNever Smoked 0.021759 *
## SmokingStatusOccasional Smoker 0.868203
## RelationshipstatusMarried 0.007166 **
## RelationshipstatusSeparated 0.199713
## RelationshipstatusSingle 0.170338
## RelationshipstatusWidowed 0.850629
## LivingstatusAssisted Living 0.075114 .
## LivingstatusHouse 0.738135
## LivingstatusOther 0.230711
## AnxietyYes 0.735738
## MoodDisordYes 0.299306
## Chronicconditions 0.167449
## PASE_TOTALbaseline 1.32e-05 ***
## RVLT_Immediate_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.401822
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_imm_adj10trun, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.26540 0.2305354 Inf 9.813563 10.71725
## FU2 data collected before COVID-19 10.43953 0.2337412 Inf 9.981409 10.89766
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.57632 0.2307399 Inf 10.124082 11.02857
## FU2 data collected before COVID-19 10.67066 0.2337327 Inf 10.212550 11.12876
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.17412870 0.08368398 Inf -2.081 0.0375
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.09433404 0.08430331 Inf -1.119 0.2631
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_imm_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.17412870 0.08368398 Inf -0.3381463 -0.01011111
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.09433404 0.08430331 Inf -0.2595655 0.07089741
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTimmediate_lsmeans_adj10trun <- summary(lsmeans(modelRVLT_imm_adj10trun, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTimmediate_lsmeans_adj10trun$Time<-NA
RVLTimmediate_lsmeans_adj10trun$Time[RVLTimmediate_lsmeans_adj10trun$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj10trun$Time[RVLTimmediate_lsmeans_adj10trun$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj10trun, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
modelRVLT_del_adj10trun<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
(1|ID), data= truncated.data_long_2)
summary(modelRVLT_del_adj10trun)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
## (1 | ID)
## Data: truncated.data_long_2
##
## REML criterion at convergence: 69370.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9097 -0.5557 -0.0353 0.5142 4.0193
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.094 2.023
## Residual 6.916 2.630
## Number of obs: 13415, groups: ID, 6961
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 6.692e+00 4.627e-01
## timefactor2 5.846e-01 6.205e-02
## PandemicFU2 data collected before COVID-19 2.559e-01 8.231e-02
## Age -8.322e-03 4.343e-03
## SexM -4.549e-01 7.530e-02
## EducationHigh School Diploma 2.210e-01 1.056e-01
## EducationLess than High School Diploma 2.909e-01 1.506e-01
## EducationSome College 3.171e-01 1.307e-01
## EthnicityWhite 6.681e-01 2.104e-01
## IncomeLevel>$150k 2.581e-01 1.929e-01
## IncomeLevel$100-150k 1.625e-01 1.566e-01
## IncomeLevel$20-50k 1.723e-01 1.057e-01
## IncomeLevel$50-100k 4.387e-01 1.136e-01
## BMI -1.830e-02 7.112e-03
## CESD.10baseline -2.062e-02 8.500e-03
## SmokingStatusFormer Smoker 1.293e-01 1.436e-01
## SmokingStatusNever Smoked 3.377e-01 1.494e-01
## SmokingStatusOccasional Smoker 2.223e-01 2.831e-01
## RelationshipstatusMarried 3.936e-02 1.237e-01
## RelationshipstatusSeparated -1.141e-01 2.398e-01
## RelationshipstatusSingle -5.020e-02 1.676e-01
## RelationshipstatusWidowed -1.397e-01 1.718e-01
## LivingstatusAssisted Living -1.257e+00 5.220e-01
## LivingstatusHouse 4.488e-02 1.128e-01
## LivingstatusOther -1.792e-01 4.192e-01
## AnxietyYes 5.259e-02 1.476e-01
## MoodDisordYes -1.189e-01 1.064e-01
## Chronicconditions -2.792e-02 1.740e-02
## PASE_TOTALbaseline 2.433e-03 5.004e-04
## RVLT_Delayed_Normedbaseline 3.953e-01 9.114e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -2.551e-01 9.182e-02
## df t value
## (Intercept) 6.969e+03 14.462
## timefactor2 6.770e+03 9.421
## PandemicFU2 data collected before COVID-19 1.178e+04 3.109
## Age 6.907e+03 -1.916
## SexM 6.890e+03 -6.042
## EducationHigh School Diploma 6.904e+03 2.093
## EducationLess than High School Diploma 7.068e+03 1.932
## EducationSome College 6.802e+03 2.427
## EthnicityWhite 6.926e+03 3.175
## IncomeLevel>$150k 6.891e+03 1.338
## IncomeLevel$100-150k 6.858e+03 1.038
## IncomeLevel$20-50k 6.917e+03 1.630
## IncomeLevel$50-100k 6.901e+03 3.860
## BMI 6.849e+03 -2.573
## CESD.10baseline 6.915e+03 -2.426
## SmokingStatusFormer Smoker 6.902e+03 0.901
## SmokingStatusNever Smoked 6.902e+03 2.260
## SmokingStatusOccasional Smoker 6.830e+03 0.785
## RelationshipstatusMarried 6.890e+03 0.318
## RelationshipstatusSeparated 6.999e+03 -0.476
## RelationshipstatusSingle 6.882e+03 -0.300
## RelationshipstatusWidowed 6.912e+03 -0.813
## LivingstatusAssisted Living 6.970e+03 -2.407
## LivingstatusHouse 6.921e+03 0.398
## LivingstatusOther 6.858e+03 -0.428
## AnxietyYes 6.870e+03 0.356
## MoodDisordYes 6.877e+03 -1.118
## Chronicconditions 6.874e+03 -1.604
## PASE_TOTALbaseline 6.889e+03 4.863
## RVLT_Delayed_Normedbaseline 6.894e+03 43.370
## timefactor2:PandemicFU2 data collected before COVID-19 6.710e+03 -2.778
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 0.001883 **
## Age 0.055388 .
## SexM 1.60e-09 ***
## EducationHigh School Diploma 0.036387 *
## EducationLess than High School Diploma 0.053383 .
## EducationSome College 0.015265 *
## EthnicityWhite 0.001505 **
## IncomeLevel>$150k 0.181073
## IncomeLevel$100-150k 0.299447
## IncomeLevel$20-50k 0.103095
## IncomeLevel$50-100k 0.000114 ***
## BMI 0.010114 *
## CESD.10baseline 0.015285 *
## SmokingStatusFormer Smoker 0.367755
## SmokingStatusNever Smoked 0.023831 *
## SmokingStatusOccasional Smoker 0.432412
## RelationshipstatusMarried 0.750375
## RelationshipstatusSeparated 0.634240
## RelationshipstatusSingle 0.764487
## RelationshipstatusWidowed 0.416002
## LivingstatusAssisted Living 0.016090 *
## LivingstatusHouse 0.690858
## LivingstatusOther 0.668932
## AnxietyYes 0.721555
## MoodDisordYes 0.263712
## Chronicconditions 0.108660
## PASE_TOTALbaseline 1.18e-06 ***
## RVLT_Delayed_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.005487 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_del_adj10trun, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.42374 0.2293039 Inf 9.974309 10.87316
## FU2 data collected before COVID-19 10.67961 0.2324530 Inf 10.224012 11.13521
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.00833 0.2294950 Inf 10.558528 11.45813
## FU2 data collected before COVID-19 11.00914 0.2324254 Inf 10.553596 11.46469
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.25587524 0.08230715 Inf -3.109 0.0019
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.00081211 0.08287378 Inf -0.010 0.9922
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_del_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.25587524 0.08230715 Inf -0.4171943 -0.09455618
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.00081211 0.08287378 Inf -0.1632417 0.16161751
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTdelayed_lsmeans_adj10trun <- summary(lsmeans(modelRVLT_del_adj10trun, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTdelayed_lsmeans_adj10trun$Time<-NA
RVLTdelayed_lsmeans_adj10trun$Time[RVLTdelayed_lsmeans_adj10trun$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj10trun$Time[RVLTdelayed_lsmeans_adj10trun$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj10trun, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
modelMAT_adj10trun<- lmer(MAT_Normed~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline +
(1|ID), data= truncated.data_long_2)
summary(modelMAT_adj10trun)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: MAT_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline + (1 | ID)
## Data: truncated.data_long_2
##
## REML criterion at convergence: 66278.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3069 -0.5275 -0.0879 0.3683 4.7473
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.248 1.499
## Residual 9.083 3.014
## Number of obs: 12616, groups: ID, 6874
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 7.818e+00 4.569e-01
## timefactor2 -1.367e+00 7.348e-02
## PandemicFU2 data collected before COVID-19 -3.648e-01 8.637e-02
## Age -1.199e-02 4.239e-03
## SexM -1.227e+00 7.340e-02
## EducationHigh School Diploma -3.722e-02 1.031e-01
## EducationLess than High School Diploma -1.354e-01 1.492e-01
## EducationSome College -2.331e-02 1.270e-01
## EthnicityWhite 9.034e-01 2.063e-01
## IncomeLevel>$150k 1.273e-01 1.881e-01
## IncomeLevel$100-150k 1.462e-01 1.526e-01
## IncomeLevel$20-50k 1.663e-01 1.036e-01
## IncomeLevel$50-100k 1.509e-01 1.114e-01
## BMI -1.323e-02 6.920e-03
## CESD.10baseline -2.175e-02 8.296e-03
## SmokingStatusFormer Smoker 8.338e-03 1.396e-01
## SmokingStatusNever Smoked -8.297e-02 1.452e-01
## SmokingStatusOccasional Smoker 1.660e-01 2.751e-01
## RelationshipstatusMarried -4.425e-03 1.210e-01
## RelationshipstatusSeparated -6.076e-02 2.328e-01
## RelationshipstatusSingle 5.469e-01 1.637e-01
## RelationshipstatusWidowed -1.947e-01 1.689e-01
## LivingstatusAssisted Living -1.228e-01 5.100e-01
## LivingstatusHouse -1.599e-01 1.106e-01
## LivingstatusOther -1.830e-01 4.123e-01
## AnxietyYes 8.310e-02 1.435e-01
## MoodDisordYes 1.138e-01 1.035e-01
## Chronicconditions -3.578e-02 1.704e-02
## PASE_TOTALbaseline -5.028e-04 4.859e-04
## MAT_Normedbaseline 4.422e-01 9.591e-03
## timefactor2:PandemicFU2 data collected before COVID-19 3.692e-01 1.087e-01
## df t value
## (Intercept) 6.718e+03 17.112
## timefactor2 6.458e+03 -18.600
## PandemicFU2 data collected before COVID-19 1.219e+04 -4.224
## Age 6.692e+03 -2.828
## SexM 6.612e+03 -16.714
## EducationHigh School Diploma 6.615e+03 -0.361
## EducationLess than High School Diploma 6.833e+03 -0.908
## EducationSome College 6.532e+03 -0.184
## EthnicityWhite 6.702e+03 4.378
## IncomeLevel>$150k 6.589e+03 0.677
## IncomeLevel$100-150k 6.576e+03 0.958
## IncomeLevel$20-50k 6.669e+03 1.606
## IncomeLevel$50-100k 6.629e+03 1.355
## BMI 6.537e+03 -1.912
## CESD.10baseline 6.592e+03 -2.621
## SmokingStatusFormer Smoker 6.546e+03 0.060
## SmokingStatusNever Smoked 6.549e+03 -0.571
## SmokingStatusOccasional Smoker 6.411e+03 0.603
## RelationshipstatusMarried 6.673e+03 -0.037
## RelationshipstatusSeparated 6.669e+03 -0.261
## RelationshipstatusSingle 6.649e+03 3.341
## RelationshipstatusWidowed 6.733e+03 -1.152
## LivingstatusAssisted Living 6.525e+03 -0.241
## LivingstatusHouse 6.683e+03 -1.446
## LivingstatusOther 6.658e+03 -0.444
## AnxietyYes 6.593e+03 0.579
## MoodDisordYes 6.558e+03 1.100
## Chronicconditions 6.630e+03 -2.100
## PASE_TOTALbaseline 6.563e+03 -1.035
## MAT_Normedbaseline 6.662e+03 46.108
## timefactor2:PandemicFU2 data collected before COVID-19 6.404e+03 3.396
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 2.42e-05 ***
## Age 0.004693 **
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.718245
## EducationLess than High School Diploma 0.364045
## EducationSome College 0.854364
## EthnicityWhite 1.22e-05 ***
## IncomeLevel>$150k 0.498705
## IncomeLevel$100-150k 0.338226
## IncomeLevel$20-50k 0.108364
## IncomeLevel$50-100k 0.175549
## BMI 0.055902 .
## CESD.10baseline 0.008775 **
## SmokingStatusFormer Smoker 0.952379
## SmokingStatusNever Smoked 0.567860
## SmokingStatusOccasional Smoker 0.546266
## RelationshipstatusMarried 0.970818
## RelationshipstatusSeparated 0.794072
## RelationshipstatusSingle 0.000839 ***
## RelationshipstatusWidowed 0.249266
## LivingstatusAssisted Living 0.809692
## LivingstatusHouse 0.148248
## LivingstatusOther 0.657210
## AnxietyYes 0.562654
## MoodDisordYes 0.271302
## Chronicconditions 0.035794 *
## PASE_TOTALbaseline 0.300789
## MAT_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.000689 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelMAT_adj10trun, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.875132 0.2254903 Inf 10.433179 11.317085
## FU2 data collected before COVID-19 10.510338 0.2286037 Inf 10.062283 10.958393
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.508377 0.2251123 Inf 9.067165 9.949589
## FU2 data collected before COVID-19 9.512742 0.2285054 Inf 9.064879 9.960604
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.3647938 0.08637129 Inf 4.224 <.0001
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0043649 0.08613823 Inf -0.051 0.9596
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelMAT_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.3647938 0.08637129 Inf 0.1955092 0.5340784
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0043649 0.08613823 Inf -0.1731927 0.1644629
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
MAT_lsmeans_adj10trun <- summary(lsmeans(modelMAT_adj10trun, ~Pandemic|timefactor))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
MAT_lsmeans_adj10trun$Time<-NA
MAT_lsmeans_adj10trun$Time[MAT_lsmeans_adj10trun$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj10trun$Time[MAT_lsmeans_adj10trun$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj10trun, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
modelAnimals_adj10trun<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
(1|ID), data= truncated.data_long_2)
summary(modelAnimals_adj10trun)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Animal_Fluency_Normed ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
## (1 | ID)
## Data: truncated.data_long_2
##
## REML criterion at convergence: 60855.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8951 -0.5615 -0.0174 0.5379 4.2565
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.361 1.536
## Residual 3.190 1.786
## Number of obs: 13639, groups: ID, 6978
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 5.013e+00 3.358e-01
## timefactor2 9.184e-02 4.175e-02
## PandemicFU2 data collected before COVID-19 1.277e-01 5.799e-02
## Age -1.342e-02 3.101e-03
## SexM -1.421e-01 5.406e-02
## EducationHigh School Diploma 1.751e-01 7.594e-02
## EducationLess than High School Diploma 1.086e-01 1.076e-01
## EducationSome College 1.726e-01 9.413e-02
## EthnicityWhite 5.258e-01 1.507e-01
## IncomeLevel>$150k -8.356e-02 1.388e-01
## IncomeLevel$100-150k -2.957e-02 1.128e-01
## IncomeLevel$20-50k -1.278e-01 7.593e-02
## IncomeLevel$50-100k -2.310e-03 8.171e-02
## BMI -8.993e-03 5.119e-03
## CESD.10baseline -1.085e-02 6.115e-03
## SmokingStatusFormer Smoker 2.019e-01 1.034e-01
## SmokingStatusNever Smoked 1.843e-01 1.075e-01
## SmokingStatusOccasional Smoker 4.889e-02 2.038e-01
## RelationshipstatusMarried 2.505e-02 8.898e-02
## RelationshipstatusSeparated 2.000e-01 1.721e-01
## RelationshipstatusSingle 7.335e-02 1.206e-01
## RelationshipstatusWidowed -7.619e-02 1.233e-01
## LivingstatusAssisted Living -1.263e-01 3.754e-01
## LivingstatusHouse 1.492e-01 8.113e-02
## LivingstatusOther -2.717e-01 3.004e-01
## AnxietyYes 9.323e-02 1.062e-01
## MoodDisordYes 5.699e-02 7.666e-02
## Chronicconditions -8.022e-03 1.251e-02
## PASE_TOTALbaseline 6.618e-04 3.594e-04
## Animal_Fluency_Normedbaseline 5.208e-01 7.853e-03
## timefactor2:PandemicFU2 data collected before COVID-19 3.455e-02 6.171e-02
## df t value
## (Intercept) 6.952e+03 14.931
## timefactor2 6.849e+03 2.200
## PandemicFU2 data collected before COVID-19 1.145e+04 2.202
## Age 6.899e+03 -4.329
## SexM 6.906e+03 -2.629
## EducationHigh School Diploma 6.918e+03 2.306
## EducationLess than High School Diploma 6.991e+03 1.009
## EducationSome College 6.862e+03 1.834
## EthnicityWhite 6.856e+03 3.489
## IncomeLevel>$150k 6.915e+03 -0.602
## IncomeLevel$100-150k 6.884e+03 -0.262
## IncomeLevel$20-50k 6.909e+03 -1.684
## IncomeLevel$50-100k 6.909e+03 -0.028
## BMI 6.889e+03 -1.757
## CESD.10baseline 6.924e+03 -1.774
## SmokingStatusFormer Smoker 6.917e+03 1.953
## SmokingStatusNever Smoked 6.916e+03 1.714
## SmokingStatusOccasional Smoker 6.843e+03 0.240
## RelationshipstatusMarried 6.942e+03 0.281
## RelationshipstatusSeparated 6.966e+03 1.162
## RelationshipstatusSingle 6.941e+03 0.608
## RelationshipstatusWidowed 6.915e+03 -0.618
## LivingstatusAssisted Living 6.968e+03 -0.337
## LivingstatusHouse 6.923e+03 1.839
## LivingstatusOther 6.785e+03 -0.904
## AnxietyYes 6.899e+03 0.878
## MoodDisordYes 6.916e+03 0.743
## Chronicconditions 6.885e+03 -0.641
## PASE_TOTALbaseline 6.897e+03 1.841
## Animal_Fluency_Normedbaseline 6.893e+03 66.315
## timefactor2:PandemicFU2 data collected before COVID-19 6.787e+03 0.560
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 0.027867 *
## PandemicFU2 data collected before COVID-19 0.027696 *
## Age 1.52e-05 ***
## SexM 0.008578 **
## EducationHigh School Diploma 0.021129 *
## EducationLess than High School Diploma 0.312993
## EducationSome College 0.066762 .
## EthnicityWhite 0.000488 ***
## IncomeLevel>$150k 0.547131
## IncomeLevel$100-150k 0.793217
## IncomeLevel$20-50k 0.092297 .
## IncomeLevel$50-100k 0.977449
## BMI 0.078991 .
## CESD.10baseline 0.076036 .
## SmokingStatusFormer Smoker 0.050850 .
## SmokingStatusNever Smoked 0.086588 .
## SmokingStatusOccasional Smoker 0.810470
## RelationshipstatusMarried 0.778341
## RelationshipstatusSeparated 0.245295
## RelationshipstatusSingle 0.543007
## RelationshipstatusWidowed 0.536662
## LivingstatusAssisted Living 0.736431
## LivingstatusHouse 0.065971 .
## LivingstatusOther 0.365781
## AnxietyYes 0.380075
## MoodDisordYes 0.457293
## Chronicconditions 0.521274
## PASE_TOTALbaseline 0.065616 .
## Animal_Fluency_Normedbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.575518
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelAnimals_adj10trun, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.814931 0.1645074 Inf 9.492503 10.13736
## FU2 data collected before COVID-19 9.942622 0.1666697 Inf 9.615955 10.26929
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.906772 0.1645825 Inf 9.584196 10.22935
## FU2 data collected before COVID-19 10.069016 0.1667465 Inf 9.742199 10.39583
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1276904 0.05799245 Inf -2.202 0.0277
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1622442 0.05841745 Inf -2.777 0.0055
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelAnimals_adj10trun, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1276904 0.05799245 Inf -0.2413535 -0.01402729
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1622442 0.05841745 Inf -0.2767403 -0.04774811
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
Animals_lsmeans_adj10trun <- summary(lsmeans(modelAnimals_adj10trun, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
Animals_lsmeans_adj10trun$Time<-NA
Animals_lsmeans_adj10trun$Time[Animals_lsmeans_adj10trun$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj10trun$Time[Animals_lsmeans_adj10trun$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj10trun, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
modelRVLT_imm_11<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= truncated.data_long)
summary(modelRVLT_imm_11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Immediate_Normed ~ timefactor * Pandemic * Age_sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: truncated.data_long
##
## REML criterion at convergence: 109081.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6731 -0.5811 -0.0495 0.5167 4.6522
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.242 2.290
## Residual 8.307 2.882
## Number of obs: 20522, groups: ID, 6987
##
## Fixed effects:
## Estimate
## (Intercept) 8.474e+00
## timefactor2 1.102e+00
## timefactor3 1.510e+00
## PandemicFU2 data collected before COVID-19 5.873e-01
## Age_sexFemales 65+ 1.243e+00
## Age_sexMales 45-64 -6.491e-01
## Age_sexMales 65+ 2.803e-01
## EducationHigh School Diploma 1.941e-01
## EducationLess than High School Diploma 6.152e-01
## EducationSome College 2.454e-01
## EthnicityWhite 1.236e+00
## IncomeLevel>$150k 8.328e-01
## IncomeLevel$100-150k 4.329e-01
## IncomeLevel$20-50k 8.769e-02
## IncomeLevel$50-100k 5.177e-01
## BMI -1.915e-02
## CESD.20.1 -3.736e-02
## SmokingStatusFormer Smoker 3.708e-01
## SmokingStatusNever Smoked 5.042e-01
## SmokingStatusOccasional Smoker 4.196e-01
## RelationshipstatusMarried 1.011e-01
## RelationshipstatusSeparated -1.735e-01
## RelationshipstatusSingle -6.697e-02
## RelationshipstatusWidowed -4.077e-02
## LivingstatusAssisted Living -3.614e-01
## LivingstatusHouse 2.335e-02
## LivingstatusOther -5.312e-01
## AnxietyYes -6.354e-02
## MoodDisordYes -8.227e-02
## Chronicconditions -2.719e-02
## PASE_TOTALbaseline 2.658e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -3.405e-01
## timefactor3:PandemicFU2 data collected before COVID-19 -5.227e-01
## timefactor2:Age_sexFemales 65+ -1.060e+00
## timefactor3:Age_sexFemales 65+ -1.554e+00
## timefactor2:Age_sexMales 45-64 -2.459e-01
## timefactor3:Age_sexMales 45-64 -1.443e-01
## timefactor2:Age_sexMales 65+ -9.250e-01
## timefactor3:Age_sexMales 65+ -1.347e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -4.720e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -2.969e-03
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -4.434e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 4.717e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 5.717e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.395e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 3.533e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3.618e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 7.819e-01
## Std. Error
## (Intercept) 3.742e-01
## timefactor2 1.239e-01
## timefactor3 1.257e-01
## PandemicFU2 data collected before COVID-19 1.536e-01
## Age_sexFemales 65+ 2.027e-01
## Age_sexMales 45-64 1.484e-01
## Age_sexMales 65+ 1.888e-01
## EducationHigh School Diploma 1.077e-01
## EducationLess than High School Diploma 1.527e-01
## EducationSome College 1.337e-01
## EthnicityWhite 2.131e-01
## IncomeLevel>$150k 1.969e-01
## IncomeLevel$100-150k 1.599e-01
## IncomeLevel$20-50k 1.080e-01
## IncomeLevel$50-100k 1.161e-01
## BMI 7.253e-03
## CESD.20.1 8.651e-03
## SmokingStatusFormer Smoker 1.466e-01
## SmokingStatusNever Smoked 1.527e-01
## SmokingStatusOccasional Smoker 2.897e-01
## RelationshipstatusMarried 1.262e-01
## RelationshipstatusSeparated 2.441e-01
## RelationshipstatusSingle 1.709e-01
## RelationshipstatusWidowed 1.749e-01
## LivingstatusAssisted Living 5.317e-01
## LivingstatusHouse 1.151e-01
## LivingstatusOther 4.282e-01
## AnxietyYes 1.506e-01
## MoodDisordYes 1.088e-01
## Chronicconditions 1.762e-02
## PASE_TOTALbaseline 4.945e-04
## timefactor2:PandemicFU2 data collected before COVID-19 1.708e-01
## timefactor3:PandemicFU2 data collected before COVID-19 1.721e-01
## timefactor2:Age_sexFemales 65+ 2.151e-01
## timefactor3:Age_sexFemales 65+ 2.182e-01
## timefactor2:Age_sexMales 45-64 1.617e-01
## timefactor3:Age_sexMales 45-64 1.632e-01
## timefactor2:Age_sexMales 65+ 2.052e-01
## timefactor3:Age_sexMales 65+ 2.085e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.676e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.232e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.631e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.976e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 3.003e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.484e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.494e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.931e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.956e-01
## df
## (Intercept) 7.423e+03
## timefactor2 1.357e+04
## timefactor3 1.368e+04
## PandemicFU2 data collected before COVID-19 1.574e+04
## Age_sexFemales 65+ 1.473e+04
## Age_sexMales 45-64 1.526e+04
## Age_sexMales 65+ 1.522e+04
## EducationHigh School Diploma 6.929e+03
## EducationLess than High School Diploma 6.990e+03
## EducationSome College 6.895e+03
## EthnicityWhite 6.895e+03
## IncomeLevel>$150k 6.931e+03
## IncomeLevel$100-150k 6.906e+03
## IncomeLevel$20-50k 6.929e+03
## IncomeLevel$50-100k 6.928e+03
## BMI 6.912e+03
## CESD.20.1 6.919e+03
## SmokingStatusFormer Smoker 6.932e+03
## SmokingStatusNever Smoked 6.933e+03
## SmokingStatusOccasional Smoker 6.859e+03
## RelationshipstatusMarried 6.932e+03
## RelationshipstatusSeparated 6.961e+03
## RelationshipstatusSingle 6.931e+03
## RelationshipstatusWidowed 6.935e+03
## LivingstatusAssisted Living 6.928e+03
## LivingstatusHouse 6.934e+03
## LivingstatusOther 6.852e+03
## AnxietyYes 6.908e+03
## MoodDisordYes 6.921e+03
## Chronicconditions 6.920e+03
## PASE_TOTALbaseline 6.912e+03
## timefactor2:PandemicFU2 data collected before COVID-19 1.356e+04
## timefactor3:PandemicFU2 data collected before COVID-19 1.362e+04
## timefactor2:Age_sexFemales 65+ 1.356e+04
## timefactor3:Age_sexFemales 65+ 1.367e+04
## timefactor2:Age_sexMales 45-64 1.357e+04
## timefactor3:Age_sexMales 45-64 1.365e+04
## timefactor2:Age_sexMales 65+ 1.357e+04
## timefactor3:Age_sexMales 65+ 1.369e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.573e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.577e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.577e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.357e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.364e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.356e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.359e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.357e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.363e+04
## t value
## (Intercept) 22.644
## timefactor2 8.895
## timefactor3 12.017
## PandemicFU2 data collected before COVID-19 3.824
## Age_sexFemales 65+ 6.130
## Age_sexMales 45-64 -4.374
## Age_sexMales 65+ 1.484
## EducationHigh School Diploma 1.803
## EducationLess than High School Diploma 4.030
## EducationSome College 1.836
## EthnicityWhite 5.801
## IncomeLevel>$150k 4.229
## IncomeLevel$100-150k 2.708
## IncomeLevel$20-50k 0.812
## IncomeLevel$50-100k 4.459
## BMI -2.640
## CESD.20.1 -4.319
## SmokingStatusFormer Smoker 2.529
## SmokingStatusNever Smoked 3.301
## SmokingStatusOccasional Smoker 1.449
## RelationshipstatusMarried 0.801
## RelationshipstatusSeparated -0.711
## RelationshipstatusSingle -0.392
## RelationshipstatusWidowed -0.233
## LivingstatusAssisted Living -0.680
## LivingstatusHouse 0.203
## LivingstatusOther -1.241
## AnxietyYes -0.422
## MoodDisordYes -0.756
## Chronicconditions -1.544
## PASE_TOTALbaseline 5.376
## timefactor2:PandemicFU2 data collected before COVID-19 -1.994
## timefactor3:PandemicFU2 data collected before COVID-19 -3.038
## timefactor2:Age_sexFemales 65+ -4.927
## timefactor3:Age_sexFemales 65+ -7.122
## timefactor2:Age_sexMales 45-64 -1.521
## timefactor3:Age_sexMales 45-64 -0.884
## timefactor2:Age_sexMales 65+ -4.507
## timefactor3:Age_sexMales 65+ -6.463
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -1.764
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.013
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.686
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.585
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.904
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.561
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.417
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.234
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.645
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 < 2e-16
## timefactor3 < 2e-16
## PandemicFU2 data collected before COVID-19 0.000132
## Age_sexFemales 65+ 8.99e-10
## Age_sexMales 45-64 1.23e-05
## Age_sexMales 65+ 0.137753
## EducationHigh School Diploma 0.071467
## EducationLess than High School Diploma 5.63e-05
## EducationSome College 0.066353
## EthnicityWhite 6.89e-09
## IncomeLevel>$150k 2.38e-05
## IncomeLevel$100-150k 0.006790
## IncomeLevel$20-50k 0.416758
## IncomeLevel$50-100k 8.35e-06
## BMI 0.008305
## CESD.20.1 1.59e-05
## SmokingStatusFormer Smoker 0.011463
## SmokingStatusNever Smoked 0.000967
## SmokingStatusOccasional Smoker 0.147515
## RelationshipstatusMarried 0.423038
## RelationshipstatusSeparated 0.477225
## RelationshipstatusSingle 0.695106
## RelationshipstatusWidowed 0.815712
## LivingstatusAssisted Living 0.496698
## LivingstatusHouse 0.839296
## LivingstatusOther 0.214786
## AnxietyYes 0.673105
## MoodDisordYes 0.449638
## Chronicconditions 0.122710
## PASE_TOTALbaseline 7.87e-08
## timefactor2:PandemicFU2 data collected before COVID-19 0.046173
## timefactor3:PandemicFU2 data collected before COVID-19 0.002388
## timefactor2:Age_sexFemales 65+ 8.47e-07
## timefactor3:Age_sexFemales 65+ 1.12e-12
## timefactor2:Age_sexMales 45-64 0.128294
## timefactor3:Age_sexMales 45-64 0.376691
## timefactor2:Age_sexMales 65+ 6.62e-06
## timefactor3:Age_sexMales 65+ 1.06e-10
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.077715
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.989385
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.091883
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.112949
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.056986
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.574482
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.156599
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.217099
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.008177
##
## (Intercept) ***
## timefactor2 ***
## timefactor3 ***
## PandemicFU2 data collected before COVID-19 ***
## Age_sexFemales 65+ ***
## Age_sexMales 45-64 ***
## Age_sexMales 65+
## EducationHigh School Diploma .
## EducationLess than High School Diploma ***
## EducationSome College .
## EthnicityWhite ***
## IncomeLevel>$150k ***
## IncomeLevel$100-150k **
## IncomeLevel$20-50k
## IncomeLevel$50-100k ***
## BMI **
## CESD.20.1 ***
## SmokingStatusFormer Smoker *
## SmokingStatusNever Smoked ***
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed
## LivingstatusAssisted Living
## LivingstatusHouse
## LivingstatusOther
## AnxietyYes
## MoodDisordYes
## Chronicconditions
## PASE_TOTALbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19 *
## timefactor3:PandemicFU2 data collected before COVID-19 **
## timefactor2:Age_sexFemales 65+ ***
## timefactor3:Age_sexFemales 65+ ***
## timefactor2:Age_sexMales 45-64
## timefactor3:Age_sexMales 45-64
## timefactor2:Age_sexMales 65+ ***
## timefactor3:Age_sexMales 65+ ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ .
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ .
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ .
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_imm_11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.418959 0.2545590 Inf 8.920033 9.917886
## FU2 data collected before COVID-19 10.006212 0.2539210 Inf 9.508536 10.503888
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.521273 0.2550483 Inf 10.021387 11.021158
## FU2 data collected before COVID-19 10.767993 0.2542857 Inf 10.269602 11.266384
##
## timefactor = 3, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.929057 0.2558349 Inf 10.427630 11.430484
## FU2 data collected before COVID-19 10.993626 0.2542776 Inf 10.495251 11.492000
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.661829 0.2793734 Inf 10.114267 11.209391
## FU2 data collected before COVID-19 10.777033 0.2791038 Inf 10.230000 11.324067
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.704509 0.2799432 Inf 10.155831 11.253188
## FU2 data collected before COVID-19 10.950906 0.2801550 Inf 10.401812 11.500000
##
## timefactor = 3, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.617637 0.2815694 Inf 10.065771 11.169503
## FU2 data collected before COVID-19 10.781843 0.2806096 Inf 10.231859 11.331828
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 8.769881 0.2457746 Inf 8.288172 9.251590
## FU2 data collected before COVID-19 9.354165 0.2638038 Inf 8.837119 9.871211
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.626316 0.2460711 Inf 9.144025 10.108606
## FU2 data collected before COVID-19 10.009555 0.2642080 Inf 9.491717 10.527393
##
## timefactor = 3, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.135684 0.2462632 Inf 9.653017 10.618351
## FU2 data collected before COVID-19 10.550537 0.2641237 Inf 10.032864 11.068210
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.699232 0.2763024 Inf 9.157690 10.240775
## FU2 data collected before COVID-19 9.843042 0.2783703 Inf 9.297446 10.388638
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.876585 0.2770129 Inf 9.333650 10.419520
## FU2 data collected before COVID-19 10.041657 0.2791889 Inf 9.494457 10.588858
##
## timefactor = 3, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.861934 0.2787644 Inf 9.315565 10.408302
## FU2 data collected before COVID-19 10.264919 0.2793393 Inf 9.717424 10.812414
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.5872533 0.1535869 Inf -3.824 0.0001
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2467203 0.1548612 Inf -1.593 0.1111
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0645682 0.1562887 Inf -0.413 0.6795
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1152043 0.2195332 Inf -0.525 0.5997
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2463965 0.2216559 Inf -1.112 0.2663
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1642059 0.2243341 Inf -0.732 0.4642
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.5842844 0.1622836 Inf -3.600 0.0003
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3832393 0.1636138 Inf -2.342 0.0192
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4148529 0.1636646 Inf -2.535 0.0113
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1438095 0.2138153 Inf -0.673 0.5012
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1650725 0.2158388 Inf -0.765 0.4444
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4029855 0.2181853 Inf -1.847 0.0647
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_imm_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.5872533 0.1535869 Inf -0.8882780 -0.28622855
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2467203 0.1548612 Inf -0.5502427 0.05680201
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0645682 0.1562887 Inf -0.3708884 0.24175194
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1152043 0.2195332 Inf -0.5454815 0.31507298
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2463965 0.2216559 Inf -0.6808341 0.18804099
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1642059 0.2243341 Inf -0.6038926 0.27548077
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.5842844 0.1622836 Inf -0.9023545 -0.26621437
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3832393 0.1636138 Inf -0.7039166 -0.06256208
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4148529 0.1636646 Inf -0.7356297 -0.09407616
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1438095 0.2138153 Inf -0.5628797 0.27526074
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1650725 0.2158388 Inf -0.5881087 0.25796377
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4029855 0.2181853 Inf -0.8306208 0.02464981
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTimmediate_lsmeans_11 <- summary(lsmeans(modelRVLT_imm_11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20522' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20522)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTimmediate_lsmeans_11$Time<-NA
RVLTimmediate_lsmeans_11$Time[RVLTimmediate_lsmeans_11$timefactor==1]<-"Baseline"
RVLTimmediate_lsmeans_11$Time[RVLTimmediate_lsmeans_11$timefactor==2]<-"Follow-up 1"
RVLTimmediate_lsmeans_11$Time[RVLTimmediate_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "RVLT Immediate Score", title = "RVLT Immediate Score from Baseline to FU2 by Pandemic status") +
theme_bw()
modelRVLT_del_11<- lmer(RVLT_Delayed_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= truncated.data_long)
summary(modelRVLT_del_11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic * Age_sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: truncated.data_long
##
## REML criterion at convergence: 107433.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9079 -0.5669 -0.0367 0.5139 4.9297
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.595 2.365
## Residual 7.637 2.763
## Number of obs: 20402, groups: ID, 6987
##
## Fixed effects:
## Estimate
## (Intercept) 9.039e+00
## timefactor2 8.822e-01
## timefactor3 1.681e+00
## PandemicFU2 data collected before COVID-19 4.730e-01
## Age_sexFemales 65+ 1.377e+00
## Age_sexMales 45-64 -5.493e-01
## Age_sexMales 65+ 6.190e-01
## EducationHigh School Diploma 3.732e-01
## EducationLess than High School Diploma 5.817e-01
## EducationSome College 3.989e-01
## EthnicityWhite 1.144e+00
## IncomeLevel>$150k 6.599e-01
## IncomeLevel$100-150k 5.069e-01
## IncomeLevel$20-50k 2.210e-01
## IncomeLevel$50-100k 5.773e-01
## BMI -1.988e-02
## CESD.20.1 -3.743e-02
## SmokingStatusFormer Smoker 1.042e-01
## SmokingStatusNever Smoked 3.396e-01
## SmokingStatusOccasional Smoker 3.041e-01
## RelationshipstatusMarried -4.634e-02
## RelationshipstatusSeparated -1.580e-01
## RelationshipstatusSingle -2.299e-01
## RelationshipstatusWidowed -7.868e-02
## LivingstatusAssisted Living -1.152e+00
## LivingstatusHouse 2.902e-03
## LivingstatusOther -3.007e-01
## AnxietyYes -5.421e-02
## MoodDisordYes -1.110e-01
## Chronicconditions -3.616e-02
## PASE_TOTALbaseline 2.737e-03
## timefactor2:PandemicFU2 data collected before COVID-19 -1.426e-01
## timefactor3:PandemicFU2 data collected before COVID-19 -5.683e-01
## timefactor2:Age_sexFemales 65+ -9.012e-01
## timefactor3:Age_sexFemales 65+ -1.519e+00
## timefactor2:Age_sexMales 45-64 -1.366e-01
## timefactor3:Age_sexMales 45-64 -2.205e-01
## timefactor2:Age_sexMales 65+ -8.688e-01
## timefactor3:Age_sexMales 65+ -1.455e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -4.786e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.409e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -3.963e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 5.582e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 9.262e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 9.290e-03
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.579e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3.406e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 7.112e-01
## Std. Error
## (Intercept) 3.774e-01
## timefactor2 1.191e-01
## timefactor3 1.205e-01
## PandemicFU2 data collected before COVID-19 1.518e-01
## Age_sexFemales 65+ 2.008e-01
## Age_sexMales 45-64 1.468e-01
## Age_sexMales 65+ 1.868e-01
## EducationHigh School Diploma 1.087e-01
## EducationLess than High School Diploma 1.542e-01
## EducationSome College 1.348e-01
## EthnicityWhite 2.157e-01
## IncomeLevel>$150k 1.986e-01
## IncomeLevel$100-150k 1.613e-01
## IncomeLevel$20-50k 1.090e-01
## IncomeLevel$50-100k 1.171e-01
## BMI 7.315e-03
## CESD.20.1 8.732e-03
## SmokingStatusFormer Smoker 1.479e-01
## SmokingStatusNever Smoked 1.540e-01
## SmokingStatusOccasional Smoker 2.925e-01
## RelationshipstatusMarried 1.274e-01
## RelationshipstatusSeparated 2.464e-01
## RelationshipstatusSingle 1.724e-01
## RelationshipstatusWidowed 1.765e-01
## LivingstatusAssisted Living 5.369e-01
## LivingstatusHouse 1.162e-01
## LivingstatusOther 4.328e-01
## AnxietyYes 1.520e-01
## MoodDisordYes 1.097e-01
## Chronicconditions 1.777e-02
## PASE_TOTALbaseline 4.992e-04
## timefactor2:PandemicFU2 data collected before COVID-19 1.641e-01
## timefactor3:PandemicFU2 data collected before COVID-19 1.651e-01
## timefactor2:Age_sexFemales 65+ 2.067e-01
## timefactor3:Age_sexFemales 65+ 2.106e-01
## timefactor2:Age_sexMales 45-64 1.555e-01
## timefactor3:Age_sexMales 45-64 1.567e-01
## timefactor2:Age_sexMales 65+ 1.979e-01
## timefactor3:Age_sexMales 65+ 2.007e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.645e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.205e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.600e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.861e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.892e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.388e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.395e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.825e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.845e-01
## df
## (Intercept) 7.404e+03
## timefactor2 1.348e+04
## timefactor3 1.356e+04
## PandemicFU2 data collected before COVID-19 1.501e+04
## Age_sexFemales 65+ 1.405e+04
## Age_sexMales 45-64 1.455e+04
## Age_sexMales 65+ 1.452e+04
## EducationHigh School Diploma 6.937e+03
## EducationLess than High School Diploma 7.024e+03
## EducationSome College 6.887e+03
## EthnicityWhite 6.969e+03
## IncomeLevel>$150k 6.928e+03
## IncomeLevel$100-150k 6.909e+03
## IncomeLevel$20-50k 6.938e+03
## IncomeLevel$50-100k 6.931e+03
## BMI 6.908e+03
## CESD.20.1 6.935e+03
## SmokingStatusFormer Smoker 6.924e+03
## SmokingStatusNever Smoked 6.926e+03
## SmokingStatusOccasional Smoker 6.884e+03
## RelationshipstatusMarried 6.942e+03
## RelationshipstatusSeparated 6.978e+03
## RelationshipstatusSingle 6.931e+03
## RelationshipstatusWidowed 6.946e+03
## LivingstatusAssisted Living 6.958e+03
## LivingstatusHouse 6.949e+03
## LivingstatusOther 6.900e+03
## AnxietyYes 6.916e+03
## MoodDisordYes 6.916e+03
## Chronicconditions 6.921e+03
## PASE_TOTALbaseline 6.925e+03
## timefactor2:PandemicFU2 data collected before COVID-19 1.346e+04
## timefactor3:PandemicFU2 data collected before COVID-19 1.351e+04
## timefactor2:Age_sexFemales 65+ 1.346e+04
## timefactor3:Age_sexFemales 65+ 1.359e+04
## timefactor2:Age_sexMales 45-64 1.348e+04
## timefactor3:Age_sexMales 45-64 1.353e+04
## timefactor2:Age_sexMales 65+ 1.349e+04
## timefactor3:Age_sexMales 65+ 1.359e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.500e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.504e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.505e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.347e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.355e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.347e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.349e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.349e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.354e+04
## t value
## (Intercept) 23.955
## timefactor2 7.405
## timefactor3 13.949
## PandemicFU2 data collected before COVID-19 3.116
## Age_sexFemales 65+ 6.860
## Age_sexMales 45-64 -3.741
## Age_sexMales 65+ 3.313
## EducationHigh School Diploma 3.435
## EducationLess than High School Diploma 3.772
## EducationSome College 2.959
## EthnicityWhite 5.307
## IncomeLevel>$150k 3.322
## IncomeLevel$100-150k 3.143
## IncomeLevel$20-50k 2.028
## IncomeLevel$50-100k 4.929
## BMI -2.718
## CESD.20.1 -4.286
## SmokingStatusFormer Smoker 0.705
## SmokingStatusNever Smoked 2.205
## SmokingStatusOccasional Smoker 1.039
## RelationshipstatusMarried -0.364
## RelationshipstatusSeparated -0.641
## RelationshipstatusSingle -1.334
## RelationshipstatusWidowed -0.446
## LivingstatusAssisted Living -2.145
## LivingstatusHouse 0.025
## LivingstatusOther -0.695
## AnxietyYes -0.357
## MoodDisordYes -1.012
## Chronicconditions -2.034
## PASE_TOTALbaseline 5.483
## timefactor2:PandemicFU2 data collected before COVID-19 -0.869
## timefactor3:PandemicFU2 data collected before COVID-19 -3.442
## timefactor2:Age_sexFemales 65+ -4.360
## timefactor3:Age_sexFemales 65+ -7.215
## timefactor2:Age_sexMales 45-64 -0.879
## timefactor3:Age_sexMales 45-64 -1.407
## timefactor2:Age_sexMales 65+ -4.391
## timefactor3:Age_sexMales 65+ -7.251
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -1.810
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.639
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.524
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.951
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 3.202
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.039
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.077
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.206
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.500
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 1.39e-13
## timefactor3 < 2e-16
## PandemicFU2 data collected before COVID-19 0.001838
## Age_sexFemales 65+ 7.16e-12
## Age_sexMales 45-64 0.000184
## Age_sexMales 65+ 0.000924
## EducationHigh School Diploma 0.000597
## EducationLess than High School Diploma 0.000163
## EducationSome College 0.003094
## EthnicityWhite 1.15e-07
## IncomeLevel>$150k 0.000898
## IncomeLevel$100-150k 0.001680
## IncomeLevel$20-50k 0.042612
## IncomeLevel$50-100k 8.44e-07
## BMI 0.006587
## CESD.20.1 1.84e-05
## SmokingStatusFormer Smoker 0.480960
## SmokingStatusNever Smoked 0.027505
## SmokingStatusOccasional Smoker 0.298616
## RelationshipstatusMarried 0.716011
## RelationshipstatusSeparated 0.521445
## RelationshipstatusSingle 0.182289
## RelationshipstatusWidowed 0.655844
## LivingstatusAssisted Living 0.031998
## LivingstatusHouse 0.980077
## LivingstatusOther 0.487184
## AnxietyYes 0.721369
## MoodDisordYes 0.311703
## Chronicconditions 0.041940
## PASE_TOTALbaseline 4.34e-08
## timefactor2:PandemicFU2 data collected before COVID-19 0.384648
## timefactor3:PandemicFU2 data collected before COVID-19 0.000579
## timefactor2:Age_sexFemales 65+ 1.31e-05
## timefactor3:Age_sexFemales 65+ 5.69e-13
## timefactor2:Age_sexMales 45-64 0.379674
## timefactor3:Age_sexMales 45-64 0.159432
## timefactor2:Age_sexMales 65+ 1.14e-05
## timefactor3:Age_sexMales 65+ 4.35e-13
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.070366
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.523004
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.127505
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.051052
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.001367
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.968962
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.281646
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.227871
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.012438
##
## (Intercept) ***
## timefactor2 ***
## timefactor3 ***
## PandemicFU2 data collected before COVID-19 **
## Age_sexFemales 65+ ***
## Age_sexMales 45-64 ***
## Age_sexMales 65+ ***
## EducationHigh School Diploma ***
## EducationLess than High School Diploma ***
## EducationSome College **
## EthnicityWhite ***
## IncomeLevel>$150k ***
## IncomeLevel$100-150k **
## IncomeLevel$20-50k *
## IncomeLevel$50-100k ***
## BMI **
## CESD.20.1 ***
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked *
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed
## LivingstatusAssisted Living *
## LivingstatusHouse
## LivingstatusOther
## AnxietyYes
## MoodDisordYes
## Chronicconditions *
## PASE_TOTALbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19
## timefactor3:PandemicFU2 data collected before COVID-19 ***
## timefactor2:Age_sexFemales 65+ ***
## timefactor3:Age_sexFemales 65+ ***
## timefactor2:Age_sexMales 45-64
## timefactor3:Age_sexMales 45-64
## timefactor2:Age_sexMales 65+ ***
## timefactor3:Age_sexMales 65+ ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ .
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ .
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ **
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_del_11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.644094 0.2561104 Inf 9.142126 10.146061
## FU2 data collected before COVID-19 10.117081 0.2555687 Inf 9.616175 10.617986
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.526324 0.2567855 Inf 10.023034 11.029614
## FU2 data collected before COVID-19 10.856667 0.2559289 Inf 10.355055 11.358278
##
## timefactor = 3, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.325388 0.2572887 Inf 10.821112 11.829665
## FU2 data collected before COVID-19 11.230056 0.2559690 Inf 10.728366 11.731746
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.021320 0.2802864 Inf 10.471969 11.570671
## FU2 data collected before COVID-19 11.015718 0.2801789 Inf 10.466577 11.564858
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.002332 0.2809530 Inf 10.451675 11.552990
## FU2 data collected before COVID-19 11.412239 0.2814886 Inf 10.860532 11.963947
##
## timefactor = 3, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.183545 0.2834169 Inf 10.628058 11.739032
## FU2 data collected before COVID-19 11.535817 0.2819040 Inf 10.983295 12.088338
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.094823 0.2475212 Inf 8.609690 9.579955
## FU2 data collected before COVID-19 9.708679 0.2650545 Inf 9.189181 10.228176
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.840437 0.2480015 Inf 9.354363 10.326511
## FU2 data collected before COVID-19 10.320939 0.2655865 Inf 9.800399 10.841479
##
## timefactor = 3, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.555664 0.2480501 Inf 10.069494 11.041833
## FU2 data collected before COVID-19 10.859056 0.2655800 Inf 10.338529 11.379583
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.263078 0.2774136 Inf 9.719357 10.806798
## FU2 data collected before COVID-19 10.339809 0.2793223 Inf 9.792348 10.887271
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.276555 0.2787166 Inf 9.730281 10.822830
## FU2 data collected before COVID-19 10.551289 0.2808669 Inf 10.000800 11.101778
##
## timefactor = 3, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.488939 0.2802947 Inf 9.939571 11.038306
## FU2 data collected before COVID-19 10.708540 0.2806653 Inf 10.158446 11.258634
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4729871 0.1518003 Inf -3.116 0.0018
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3303426 0.1533256 Inf -2.155 0.0312
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.0953322 0.1544562 Inf 0.617 0.5371
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.0056028 0.2170057 Inf 0.026 0.9794
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4099067 0.2196553 Inf -1.866 0.0620
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3522712 0.2230039 Inf -1.580 0.1142
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.6138560 0.1603956 Inf -3.827 0.0001
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4805016 0.1621041 Inf -2.964 0.0030
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3033924 0.1621468 Inf -1.871 0.0613
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0767316 0.2113181 Inf -0.363 0.7165
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2747335 0.2148483 Inf -1.279 0.2010
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2196013 0.2166938 Inf -1.013 0.3109
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_del_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4729871 0.1518003 Inf -0.7705103 -0.1754639
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3303426 0.1533256 Inf -0.6308553 -0.0298298
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.0953322 0.1544562 Inf -0.2073964 0.3980607
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.0056028 0.2170057 Inf -0.4197205 0.4309261
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4099067 0.2196553 Inf -0.8404232 0.0206098
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3522712 0.2230039 Inf -0.7893508 0.0848085
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.6138560 0.1603956 Inf -0.9282256 -0.2994863
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4805016 0.1621041 Inf -0.7982198 -0.1627833
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3033924 0.1621468 Inf -0.6211943 0.0144094
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0767316 0.2113181 Inf -0.4909074 0.3374441
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2747335 0.2148483 Inf -0.6958284 0.1463614
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2196013 0.2166938 Inf -0.6443133 0.2051108
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTdelayed_lsmeans_11 <- summary(lsmeans(modelRVLT_del_11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20402' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20402)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTdelayed_lsmeans_11$Time<-NA
RVLTdelayed_lsmeans_11$Time[RVLTdelayed_lsmeans_11$timefactor==1]<-"Baseline"
RVLTdelayed_lsmeans_11$Time[RVLTdelayed_lsmeans_11$timefactor==2]<-"Follow-up 1"
RVLTdelayed_lsmeans_11$Time[RVLTdelayed_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "RVLT Delayed Score", title = "RVLT Delayed Score from Baseline to FU2 by Pandemic status") +
theme_bw()
modelMAT_11<- lmer(MAT_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= truncated.data_long)
summary(modelMAT_11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## MAT_Normed ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +
## IncomeLevel + BMI + CESD.20.1 + SmokingStatus + Relationshipstatus +
## Livingstatus + Anxiety + MoodDisord + Chronicconditions +
## PASE_TOTALbaseline + (1 | ID)
## Data: truncated.data_long
##
## REML criterion at convergence: 102813.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9402 -0.4850 0.0013 0.4499 4.4706
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 5.054 2.248
## Residual 7.614 2.759
## Number of obs: 19603, groups: ID, 6987
##
## Fixed effects:
## Estimate
## (Intercept) 9.338e+00
## timefactor2 2.951e+00
## timefactor3 -2.984e-01
## PandemicFU2 data collected before COVID-19 3.757e-02
## Age_sexFemales 65+ 2.382e-01
## Age_sexMales 45-64 -4.012e-01
## Age_sexMales 65+ -5.418e-01
## EducationHigh School Diploma -7.027e-03
## EducationLess than High School Diploma -4.984e-02
## EducationSome College 6.346e-02
## EthnicityWhite 1.352e+00
## IncomeLevel>$150k 7.361e-01
## IncomeLevel$100-150k 8.394e-01
## IncomeLevel$20-50k 3.332e-01
## IncomeLevel$50-100k 6.743e-01
## BMI -2.222e-02
## CESD.20.1 -4.779e-02
## SmokingStatusFormer Smoker 1.958e-01
## SmokingStatusNever Smoked 1.419e-01
## SmokingStatusOccasional Smoker 1.829e-01
## RelationshipstatusMarried 1.626e-01
## RelationshipstatusSeparated -1.999e-01
## RelationshipstatusSingle 2.736e-01
## RelationshipstatusWidowed -2.039e-01
## LivingstatusAssisted Living -5.060e-01
## LivingstatusHouse -1.712e-01
## LivingstatusOther -3.122e-01
## AnxietyYes 1.055e-01
## MoodDisordYes 3.021e-01
## Chronicconditions -6.112e-02
## PASE_TOTALbaseline -8.248e-04
## timefactor2:PandemicFU2 data collected before COVID-19 -1.278e+00
## timefactor3:PandemicFU2 data collected before COVID-19 1.399e-01
## timefactor2:Age_sexFemales 65+ -7.824e-01
## timefactor3:Age_sexFemales 65+ -2.864e-01
## timefactor2:Age_sexMales 45-64 -3.089e+00
## timefactor3:Age_sexMales 45-64 2.023e-01
## timefactor2:Age_sexMales 65+ -3.042e+00
## timefactor3:Age_sexMales 65+ -1.597e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -2.496e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.077e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3.650e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.816e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 3.125e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.247e+00
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -2.293e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.338e+00
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.615e-02
## Std. Error
## (Intercept) 3.681e-01
## timefactor2 1.207e-01
## timefactor3 1.217e-01
## PandemicFU2 data collected before COVID-19 1.485e-01
## Age_sexFemales 65+ 1.964e-01
## Age_sexMales 45-64 1.436e-01
## Age_sexMales 65+ 1.828e-01
## EducationHigh School Diploma 1.060e-01
## EducationLess than High School Diploma 1.511e-01
## EducationSome College 1.312e-01
## EthnicityWhite 2.105e-01
## IncomeLevel>$150k 1.935e-01
## IncomeLevel$100-150k 1.571e-01
## IncomeLevel$20-50k 1.063e-01
## IncomeLevel$50-100k 1.143e-01
## BMI 7.124e-03
## CESD.20.1 8.510e-03
## SmokingStatusFormer Smoker 1.440e-01
## SmokingStatusNever Smoked 1.499e-01
## SmokingStatusOccasional Smoker 2.848e-01
## RelationshipstatusMarried 1.242e-01
## RelationshipstatusSeparated 2.396e-01
## RelationshipstatusSingle 1.681e-01
## RelationshipstatusWidowed 1.725e-01
## LivingstatusAssisted Living 5.237e-01
## LivingstatusHouse 1.134e-01
## LivingstatusOther 4.231e-01
## AnxietyYes 1.480e-01
## MoodDisordYes 1.068e-01
## Chronicconditions 1.734e-02
## PASE_TOTALbaseline 4.859e-04
## timefactor2:PandemicFU2 data collected before COVID-19 1.664e-01
## timefactor3:PandemicFU2 data collected before COVID-19 1.665e-01
## timefactor2:Age_sexFemales 65+ 2.132e-01
## timefactor3:Age_sexFemales 65+ 2.152e-01
## timefactor2:Age_sexMales 45-64 1.578e-01
## timefactor3:Age_sexMales 45-64 1.583e-01
## timefactor2:Age_sexMales 65+ 2.031e-01
## timefactor3:Age_sexMales 65+ 2.070e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.588e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.158e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.544e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.940e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.949e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.432e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.419e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.901e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.914e-01
## df
## (Intercept) 7.372e+03
## timefactor2 1.279e+04
## timefactor3 1.285e+04
## PandemicFU2 data collected before COVID-19 1.498e+04
## Age_sexFemales 65+ 1.405e+04
## Age_sexMales 45-64 1.454e+04
## Age_sexMales 65+ 1.450e+04
## EducationHigh School Diploma 6.892e+03
## EducationLess than High School Diploma 7.068e+03
## EducationSome College 6.815e+03
## EthnicityWhite 6.948e+03
## IncomeLevel>$150k 6.863e+03
## IncomeLevel$100-150k 6.841e+03
## IncomeLevel$20-50k 6.911e+03
## IncomeLevel$50-100k 6.892e+03
## BMI 6.836e+03
## CESD.20.1 6.870e+03
## SmokingStatusFormer Smoker 6.838e+03
## SmokingStatusNever Smoked 6.839e+03
## SmokingStatusOccasional Smoker 6.794e+03
## RelationshipstatusMarried 6.909e+03
## RelationshipstatusSeparated 6.875e+03
## RelationshipstatusSingle 6.891e+03
## RelationshipstatusWidowed 6.952e+03
## LivingstatusAssisted Living 6.892e+03
## LivingstatusHouse 6.925e+03
## LivingstatusOther 6.909e+03
## AnxietyYes 6.842e+03
## MoodDisordYes 6.836e+03
## Chronicconditions 6.892e+03
## PASE_TOTALbaseline 6.841e+03
## timefactor2:PandemicFU2 data collected before COVID-19 1.279e+04
## timefactor3:PandemicFU2 data collected before COVID-19 1.279e+04
## timefactor2:Age_sexFemales 65+ 1.293e+04
## timefactor3:Age_sexFemales 65+ 1.299e+04
## timefactor2:Age_sexMales 45-64 1.281e+04
## timefactor3:Age_sexMales 45-64 1.284e+04
## timefactor2:Age_sexMales 65+ 1.295e+04
## timefactor3:Age_sexMales 65+ 1.308e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.497e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.501e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.501e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.291e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.293e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.282e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.278e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.294e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.297e+04
## t value
## (Intercept) 25.370
## timefactor2 24.446
## timefactor3 -2.453
## PandemicFU2 data collected before COVID-19 0.253
## Age_sexFemales 65+ 1.213
## Age_sexMales 45-64 -2.794
## Age_sexMales 65+ -2.965
## EducationHigh School Diploma -0.066
## EducationLess than High School Diploma -0.330
## EducationSome College 0.484
## EthnicityWhite 6.422
## IncomeLevel>$150k 3.803
## IncomeLevel$100-150k 5.344
## IncomeLevel$20-50k 3.133
## IncomeLevel$50-100k 5.902
## BMI -3.118
## CESD.20.1 -5.615
## SmokingStatusFormer Smoker 1.360
## SmokingStatusNever Smoked 0.946
## SmokingStatusOccasional Smoker 0.642
## RelationshipstatusMarried 1.309
## RelationshipstatusSeparated -0.834
## RelationshipstatusSingle 1.628
## RelationshipstatusWidowed -1.182
## LivingstatusAssisted Living -0.966
## LivingstatusHouse -1.509
## LivingstatusOther -0.738
## AnxietyYes 0.713
## MoodDisordYes 2.827
## Chronicconditions -3.524
## PASE_TOTALbaseline -1.698
## timefactor2:PandemicFU2 data collected before COVID-19 -7.680
## timefactor3:PandemicFU2 data collected before COVID-19 0.840
## timefactor2:Age_sexFemales 65+ -3.671
## timefactor3:Age_sexFemales 65+ -1.331
## timefactor2:Age_sexMales 45-64 -19.582
## timefactor3:Age_sexMales 45-64 1.278
## timefactor2:Age_sexMales 65+ -14.982
## timefactor3:Age_sexMales 65+ -0.771
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -0.965
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.499
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.435
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.618
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.060
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 5.128
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.948
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 4.614
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.055
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 < 2e-16
## timefactor3 0.014183
## PandemicFU2 data collected before COVID-19 0.800289
## Age_sexFemales 65+ 0.225235
## Age_sexMales 45-64 0.005215
## Age_sexMales 65+ 0.003035
## EducationHigh School Diploma 0.947137
## EducationLess than High School Diploma 0.741547
## EducationSome College 0.628671
## EthnicityWhite 1.43e-10
## IncomeLevel>$150k 0.000144
## IncomeLevel$100-150k 9.36e-08
## IncomeLevel$20-50k 0.001739
## IncomeLevel$50-100k 3.76e-09
## BMI 0.001827
## CESD.20.1 2.04e-08
## SmokingStatusFormer Smoker 0.173766
## SmokingStatusNever Smoked 0.344043
## SmokingStatusOccasional Smoker 0.520812
## RelationshipstatusMarried 0.190685
## RelationshipstatusSeparated 0.404098
## RelationshipstatusSingle 0.103626
## RelationshipstatusWidowed 0.237253
## LivingstatusAssisted Living 0.334010
## LivingstatusHouse 0.131231
## LivingstatusOther 0.460635
## AnxietyYes 0.476064
## MoodDisordYes 0.004709
## Chronicconditions 0.000428
## PASE_TOTALbaseline 0.089647
## timefactor2:PandemicFU2 data collected before COVID-19 1.70e-14
## timefactor3:PandemicFU2 data collected before COVID-19 0.400722
## timefactor2:Age_sexFemales 65+ 0.000243
## timefactor3:Age_sexFemales 65+ 0.183254
## timefactor2:Age_sexMales 45-64 < 2e-16
## timefactor3:Age_sexMales 45-64 0.201367
## timefactor2:Age_sexMales 65+ < 2e-16
## timefactor3:Age_sexMales 65+ 0.440547
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.334782
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.617756
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.151315
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.536820
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.289213
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.97e-07
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.343270
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3.99e-06
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.955809
##
## (Intercept) ***
## timefactor2 ***
## timefactor3 *
## PandemicFU2 data collected before COVID-19
## Age_sexFemales 65+
## Age_sexMales 45-64 **
## Age_sexMales 65+ **
## EducationHigh School Diploma
## EducationLess than High School Diploma
## EducationSome College
## EthnicityWhite ***
## IncomeLevel>$150k ***
## IncomeLevel$100-150k ***
## IncomeLevel$20-50k **
## IncomeLevel$50-100k ***
## BMI **
## CESD.20.1 ***
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed
## LivingstatusAssisted Living
## LivingstatusHouse
## LivingstatusOther
## AnxietyYes
## MoodDisordYes **
## Chronicconditions ***
## PASE_TOTALbaseline .
## timefactor2:PandemicFU2 data collected before COVID-19 ***
## timefactor3:PandemicFU2 data collected before COVID-19
## timefactor2:Age_sexFemales 65+ ***
## timefactor3:Age_sexFemales 65+
## timefactor2:Age_sexMales 45-64 ***
## timefactor3:Age_sexMales 45-64
## timefactor2:Age_sexMales 65+ ***
## timefactor3:Age_sexMales 65+
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 ***
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ ***
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelMAT_11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.486104 0.2500419 Inf 8.996031 9.976177
## FU2 data collected before COVID-19 9.523677 0.2494929 Inf 9.034680 10.012674
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 12.436735 0.2516303 Inf 11.943549 12.929921
## FU2 data collected before COVID-19 11.196007 0.2507834 Inf 10.704480 11.687533
##
## timefactor = 3, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.187691 0.2518532 Inf 8.694067 9.681314
## FU2 data collected before COVID-19 9.365190 0.2503619 Inf 8.874489 9.855890
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.724277 0.2738024 Inf 9.187635 10.260920
## FU2 data collected before COVID-19 9.512264 0.2736716 Inf 8.975877 10.048650
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.892462 0.2788904 Inf 11.345847 12.439077
## FU2 data collected before COVID-19 10.583713 0.2776223 Inf 10.039583 11.127843
##
## timefactor = 3, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.139463 0.2794253 Inf 8.591799 9.687126
## FU2 data collected before COVID-19 9.379889 0.2774931 Inf 8.836012 9.923765
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.084873 0.2416041 Inf 8.611338 9.558408
## FU2 data collected before COVID-19 9.230134 0.2588066 Inf 8.722882 9.737385
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 8.946023 0.2429148 Inf 8.469919 9.422127
## FU2 data collected before COVID-19 9.060055 0.2614040 Inf 8.547713 9.572398
##
## timefactor = 3, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 8.988778 0.2426544 Inf 8.513184 9.464372
## FU2 data collected before COVID-19 9.044675 0.2603743 Inf 8.534350 9.554999
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 8.944304 0.2709466 Inf 8.413258 9.475350
## FU2 data collected before COVID-19 9.346916 0.2728221 Inf 8.812195 9.881638
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 8.852732 0.2758962 Inf 8.311985 9.393479
## FU2 data collected before COVID-19 9.315246 0.2778603 Inf 8.770650 9.859842
##
## timefactor = 3, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 8.486221 0.2780339 Inf 7.941284 9.031157
## FU2 data collected before COVID-19 9.044905 0.2768707 Inf 8.502248 9.587562
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0375734 0.1485249 Inf -0.253 0.8003
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 1.2407284 0.1529348 Inf 8.113 <.0001
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1774989 0.1530202 Inf -1.160 0.2461
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.2120136 0.2123251 Inf 0.999 0.3180
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 1.3087492 0.2241153 Inf 5.840 <.0001
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2404263 0.2251556 Inf -1.068 0.2856
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1452606 0.1569348 Inf -0.926 0.3546
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1140322 0.1631734 Inf -0.699 0.4847
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0558966 0.1612160 Inf -0.347 0.7288
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4026124 0.2067627 Inf -1.947 0.0515
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.4625139 0.2189963 Inf -2.112 0.0347
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.5586843 0.2206571 Inf -2.532 0.0113
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelMAT_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0375734 0.1485249 Inf -0.3286770 0.2535301
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 1.2407284 0.1529348 Inf 0.9409817 1.5404750
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1774989 0.1530202 Inf -0.4774131 0.1224153
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.2120136 0.2123251 Inf -0.2041358 0.6281631
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 1.3087492 0.2241153 Inf 0.8694913 1.7480071
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2404263 0.2251556 Inf -0.6817231 0.2008705
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1452606 0.1569348 Inf -0.4528471 0.1623259
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1140322 0.1631734 Inf -0.4338462 0.2057818
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0558966 0.1612160 Inf -0.3718741 0.2600810
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4026124 0.2067627 Inf -0.8078598 0.0026349
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.4625139 0.2189963 Inf -0.8917387 -0.0332891
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.5586843 0.2206571 Inf -0.9911642 -0.1262044
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
MAT_lsmeans_11 <- summary(lsmeans(modelMAT_11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19603' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19603)' or larger];
## but be warned that this may result in large computation time and memory use.
MAT_lsmeans_11$Time<-NA
MAT_lsmeans_11$Time[MAT_lsmeans_11$timefactor==1]<-"Baseline"
MAT_lsmeans_11$Time[MAT_lsmeans_11$timefactor==2]<-"Follow-up 1"
MAT_lsmeans_11$Time[MAT_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(MAT_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "MAT Score", title = "Mental Alteration Test Score from Baseline to FU2 by Pandemic status") +
theme_bw()
modelAnimals_11<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= truncated.data_long)
summary(modelAnimals_11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Animal_Fluency_Normed ~ timefactor * Pandemic * Age_sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: truncated.data_long
##
## REML criterion at convergence: 96426.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3008 -0.5637 -0.0115 0.5550 4.3819
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.873 2.207
## Residual 3.634 1.906
## Number of obs: 20626, groups: ID, 6987
##
## Fixed effects:
## Estimate
## (Intercept) 8.762e+00
## timefactor2 -5.881e-02
## timefactor3 2.526e-01
## PandemicFU2 data collected before COVID-19 2.930e-01
## Age_sexFemales 65+ 2.547e-01
## Age_sexMales 45-64 -5.258e-02
## Age_sexMales 65+ -1.177e-01
## EducationHigh School Diploma 3.350e-01
## EducationLess than High School Diploma 3.501e-01
## EducationSome College 2.938e-01
## EthnicityWhite 1.383e+00
## IncomeLevel>$150k 5.116e-01
## IncomeLevel$100-150k 4.084e-01
## IncomeLevel$20-50k 6.383e-02
## IncomeLevel$50-100k 2.534e-01
## BMI -1.789e-02
## CESD.20.1 -3.612e-02
## SmokingStatusFormer Smoker 3.165e-01
## SmokingStatusNever Smoked 2.612e-01
## SmokingStatusOccasional Smoker 4.441e-01
## RelationshipstatusMarried -2.526e-01
## RelationshipstatusSeparated -9.152e-02
## RelationshipstatusSingle -1.653e-01
## RelationshipstatusWidowed -2.627e-01
## LivingstatusAssisted Living -2.214e-01
## LivingstatusHouse 3.471e-01
## LivingstatusOther 2.311e-01
## AnxietyYes -5.049e-02
## MoodDisordYes 2.472e-01
## Chronicconditions -1.836e-03
## PASE_TOTALbaseline 9.430e-04
## timefactor2:PandemicFU2 data collected before COVID-19 8.401e-02
## timefactor3:PandemicFU2 data collected before COVID-19 -4.249e-02
## timefactor2:Age_sexFemales 65+ -3.629e-01
## timefactor3:Age_sexFemales 65+ -7.586e-01
## timefactor2:Age_sexMales 45-64 -2.952e-02
## timefactor3:Age_sexMales 45-64 -2.190e-01
## timefactor2:Age_sexMales 65+ -1.242e-01
## timefactor3:Age_sexMales 65+ -6.605e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -1.301e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.133e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.568e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 8.826e-02
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.500e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -2.384e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.066e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.079e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.680e-01
## Std. Error
## (Intercept) 3.230e-01
## timefactor2 8.197e-02
## timefactor3 8.299e-02
## PandemicFU2 data collected before COVID-19 1.218e-01
## Age_sexFemales 65+ 1.622e-01
## Age_sexMales 45-64 1.182e-01
## Age_sexMales 65+ 1.504e-01
## EducationHigh School Diploma 9.361e-02
## EducationLess than High School Diploma 1.325e-01
## EducationSome College 1.162e-01
## EthnicityWhite 1.853e-01
## IncomeLevel>$150k 1.711e-01
## IncomeLevel$100-150k 1.390e-01
## IncomeLevel$20-50k 9.383e-02
## IncomeLevel$50-100k 1.009e-01
## BMI 6.305e-03
## CESD.20.1 7.522e-03
## SmokingStatusFormer Smoker 1.274e-01
## SmokingStatusNever Smoked 1.327e-01
## SmokingStatusOccasional Smoker 2.522e-01
## RelationshipstatusMarried 1.097e-01
## RelationshipstatusSeparated 2.120e-01
## RelationshipstatusSingle 1.485e-01
## RelationshipstatusWidowed 1.520e-01
## LivingstatusAssisted Living 4.624e-01
## LivingstatusHouse 1.001e-01
## LivingstatusOther 3.725e-01
## AnxietyYes 1.310e-01
## MoodDisordYes 9.460e-02
## Chronicconditions 1.531e-02
## PASE_TOTALbaseline 4.300e-04
## timefactor2:PandemicFU2 data collected before COVID-19 1.128e-01
## timefactor3:PandemicFU2 data collected before COVID-19 1.136e-01
## timefactor2:Age_sexFemales 65+ 1.419e-01
## timefactor3:Age_sexFemales 65+ 1.442e-01
## timefactor2:Age_sexMales 45-64 1.069e-01
## timefactor3:Age_sexMales 45-64 1.079e-01
## timefactor2:Age_sexMales 65+ 1.354e-01
## timefactor3:Age_sexMales 65+ 1.376e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.122e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.769e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.085e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.961e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.980e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.642e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.648e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.933e-01
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.951e-01
## df
## (Intercept) 7.245e+03
## timefactor2 1.367e+04
## timefactor3 1.373e+04
## PandemicFU2 data collected before COVID-19 1.245e+04
## Age_sexFemales 65+ 1.171e+04
## Age_sexMales 45-64 1.209e+04
## Age_sexMales 65+ 1.206e+04
## EducationHigh School Diploma 6.959e+03
## EducationLess than High School Diploma 6.979e+03
## EducationSome College 6.933e+03
## EthnicityWhite 6.939e+03
## IncomeLevel>$150k 6.951e+03
## IncomeLevel$100-150k 6.944e+03
## IncomeLevel$20-50k 6.950e+03
## IncomeLevel$50-100k 6.951e+03
## BMI 6.941e+03
## CESD.20.1 6.954e+03
## SmokingStatusFormer Smoker 6.951e+03
## SmokingStatusNever Smoked 6.951e+03
## SmokingStatusOccasional Smoker 6.920e+03
## RelationshipstatusMarried 6.966e+03
## RelationshipstatusSeparated 6.970e+03
## RelationshipstatusSingle 6.962e+03
## RelationshipstatusWidowed 6.951e+03
## LivingstatusAssisted Living 6.970e+03
## LivingstatusHouse 6.960e+03
## LivingstatusOther 6.898e+03
## AnxietyYes 6.946e+03
## MoodDisordYes 6.953e+03
## Chronicconditions 6.943e+03
## PASE_TOTALbaseline 6.945e+03
## timefactor2:PandemicFU2 data collected before COVID-19 1.366e+04
## timefactor3:PandemicFU2 data collected before COVID-19 1.369e+04
## timefactor2:Age_sexFemales 65+ 1.365e+04
## timefactor3:Age_sexFemales 65+ 1.372e+04
## timefactor2:Age_sexMales 45-64 1.366e+04
## timefactor3:Age_sexMales 45-64 1.371e+04
## timefactor2:Age_sexMales 65+ 1.365e+04
## timefactor3:Age_sexMales 65+ 1.373e+04
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.244e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.247e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.247e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.364e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.369e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.366e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.367e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.365e+04
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.369e+04
## t value
## (Intercept) 27.128
## timefactor2 -0.717
## timefactor3 3.044
## PandemicFU2 data collected before COVID-19 2.406
## Age_sexFemales 65+ 1.571
## Age_sexMales 45-64 -0.445
## Age_sexMales 65+ -0.783
## EducationHigh School Diploma 3.579
## EducationLess than High School Diploma 2.643
## EducationSome College 2.528
## EthnicityWhite 7.463
## IncomeLevel>$150k 2.990
## IncomeLevel$100-150k 2.938
## IncomeLevel$20-50k 0.680
## IncomeLevel$50-100k 2.512
## BMI -2.837
## CESD.20.1 -4.802
## SmokingStatusFormer Smoker 2.484
## SmokingStatusNever Smoked 1.969
## SmokingStatusOccasional Smoker 1.761
## RelationshipstatusMarried -2.302
## RelationshipstatusSeparated -0.432
## RelationshipstatusSingle -1.113
## RelationshipstatusWidowed -1.729
## LivingstatusAssisted Living -0.479
## LivingstatusHouse 3.470
## LivingstatusOther 0.620
## AnxietyYes -0.385
## MoodDisordYes 2.613
## Chronicconditions -0.120
## PASE_TOTALbaseline 2.193
## timefactor2:PandemicFU2 data collected before COVID-19 0.744
## timefactor3:PandemicFU2 data collected before COVID-19 -0.374
## timefactor2:Age_sexFemales 65+ -2.557
## timefactor3:Age_sexFemales 65+ -5.262
## timefactor2:Age_sexMales 45-64 -0.276
## timefactor3:Age_sexMales 45-64 -2.029
## timefactor2:Age_sexMales 65+ -0.917
## timefactor3:Age_sexMales 65+ -4.799
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -0.613
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -0.640
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -0.752
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.450
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.263
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.452
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.065
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -0.558
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.374
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 0.473078
## timefactor3 0.002342
## PandemicFU2 data collected before COVID-19 0.016122
## Age_sexFemales 65+ 0.116272
## Age_sexMales 45-64 0.656373
## Age_sexMales 65+ 0.433794
## EducationHigh School Diploma 0.000348
## EducationLess than High School Diploma 0.008244
## EducationSome College 0.011483
## EthnicityWhite 9.46e-14
## IncomeLevel>$150k 0.002804
## IncomeLevel$100-150k 0.003315
## IncomeLevel$20-50k 0.496393
## IncomeLevel$50-100k 0.012029
## BMI 0.004563
## CESD.20.1 1.60e-06
## SmokingStatusFormer Smoker 0.013027
## SmokingStatusNever Smoked 0.049050
## SmokingStatusOccasional Smoker 0.078294
## RelationshipstatusMarried 0.021349
## RelationshipstatusSeparated 0.665962
## RelationshipstatusSingle 0.265836
## RelationshipstatusWidowed 0.083880
## LivingstatusAssisted Living 0.632181
## LivingstatusHouse 0.000524
## LivingstatusOther 0.535037
## AnxietyYes 0.699898
## MoodDisordYes 0.009005
## Chronicconditions 0.904546
## PASE_TOTALbaseline 0.028331
## timefactor2:PandemicFU2 data collected before COVID-19 0.456590
## timefactor3:PandemicFU2 data collected before COVID-19 0.708424
## timefactor2:Age_sexFemales 65+ 0.010581
## timefactor3:Age_sexFemales 65+ 1.45e-07
## timefactor2:Age_sexMales 45-64 0.782392
## timefactor3:Age_sexMales 45-64 0.042476
## timefactor2:Age_sexMales 65+ 0.359024
## timefactor3:Age_sexMales 65+ 1.61e-06
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.539768
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.521989
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.452179
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.652646
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.206708
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.146464
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.948430
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.576655
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.169546
##
## (Intercept) ***
## timefactor2
## timefactor3 **
## PandemicFU2 data collected before COVID-19 *
## Age_sexFemales 65+
## Age_sexMales 45-64
## Age_sexMales 65+
## EducationHigh School Diploma ***
## EducationLess than High School Diploma **
## EducationSome College *
## EthnicityWhite ***
## IncomeLevel>$150k **
## IncomeLevel$100-150k **
## IncomeLevel$20-50k
## IncomeLevel$50-100k *
## BMI **
## CESD.20.1 ***
## SmokingStatusFormer Smoker *
## SmokingStatusNever Smoked *
## SmokingStatusOccasional Smoker .
## RelationshipstatusMarried *
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed .
## LivingstatusAssisted Living
## LivingstatusHouse ***
## LivingstatusOther
## AnxietyYes
## MoodDisordYes **
## Chronicconditions
## PASE_TOTALbaseline *
## timefactor2:PandemicFU2 data collected before COVID-19
## timefactor3:PandemicFU2 data collected before COVID-19
## timefactor2:Age_sexFemales 65+ *
## timefactor3:Age_sexFemales 65+ ***
## timefactor2:Age_sexMales 45-64
## timefactor3:Age_sexMales 45-64 *
## timefactor2:Age_sexMales 65+
## timefactor3:Age_sexMales 65+ ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 48 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Significant differences for females 45-64 and 65+ and significant differences males 65+
lsmeans(modelAnimals_11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.732812 0.2178217 Inf 9.305889 10.159734
## FU2 data collected before COVID-19 10.025847 0.2176155 Inf 9.599329 10.452366
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.674000 0.2180696 Inf 9.246591 10.101409
## FU2 data collected before COVID-19 10.051050 0.2177402 Inf 9.624287 10.477812
##
## timefactor = 3, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.985407 0.2184097 Inf 9.557331 10.413482
## FU2 data collected before COVID-19 10.235955 0.2177660 Inf 9.809141 10.662768
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.987538 0.2363308 Inf 9.524338 10.450738
## FU2 data collected before COVID-19 10.150483 0.2366661 Inf 9.686626 10.614340
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.565831 0.2364584 Inf 9.102381 10.029281
## FU2 data collected before COVID-19 9.901049 0.2367851 Inf 9.436959 10.365139
##
## timefactor = 3, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.481578 0.2373316 Inf 9.016417 9.946739
## FU2 data collected before COVID-19 9.852008 0.2370916 Inf 9.387317 10.316699
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.680237 0.2111884 Inf 9.266315 10.094158
## FU2 data collected before COVID-19 9.860018 0.2245080 Inf 9.419990 10.300045
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.591902 0.2113467 Inf 9.177670 10.006134
## FU2 data collected before COVID-19 9.617296 0.2246682 Inf 9.176954 10.057637
##
## timefactor = 3, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.713859 0.2114756 Inf 9.299374 10.128343
## FU2 data collected before COVID-19 9.861810 0.2246404 Inf 9.421523 10.302097
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.615090 0.2345247 Inf 9.155430 10.074750
## FU2 data collected before COVID-19 9.751365 0.2356662 Inf 9.289468 10.213262
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.432041 0.2347164 Inf 8.972006 9.892077
## FU2 data collected before COVID-19 9.544407 0.2358416 Inf 9.082166 10.006648
##
## timefactor = 3, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.207144 0.2356735 Inf 8.745233 9.669056
## FU2 data collected before COVID-19 9.568911 0.2360187 Inf 9.106323 10.031499
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2930357 0.1217698 Inf -2.406 0.0161
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3770496 0.1223617 Inf -3.081 0.0021
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2505482 0.1230737 Inf -2.036 0.0418
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1629443 0.1741475 Inf -0.936 0.3494
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3352180 0.1745390 Inf -1.921 0.0548
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3704299 0.1761686 Inf -2.103 0.0355
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1797814 0.1286643 Inf -1.397 0.1623
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0253933 0.1292888 Inf -0.196 0.8443
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1479514 0.1293990 Inf -1.143 0.2529
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1362749 0.1694833 Inf -0.804 0.4214
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1123653 0.1700299 Inf -0.661 0.5087
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3617668 0.1715347 Inf -2.109 0.0349
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelAnimals_11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2930357 0.1217698 Inf -0.5317001 -0.05437130
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3770496 0.1223617 Inf -0.6168741 -0.13722505
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2505482 0.1230737 Inf -0.4917681 -0.00932832
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1629443 0.1741475 Inf -0.5042671 0.17837850
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3352180 0.1745390 Inf -0.6773081 0.00687208
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3704299 0.1761686 Inf -0.7157140 -0.02514580
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1797814 0.1286643 Inf -0.4319588 0.07239596
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0253933 0.1292888 Inf -0.2787947 0.22800805
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1479514 0.1293990 Inf -0.4015688 0.10566592
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1362749 0.1694833 Inf -0.4684562 0.19590626
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1123653 0.1700299 Inf -0.4456178 0.22088731
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3617668 0.1715347 Inf -0.6979686 -0.02556500
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
Animals_lsmeans_11 <- summary(lsmeans(modelAnimals_11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 20626' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 20626)' or larger];
## but be warned that this may result in large computation time and memory use.
Animals_lsmeans_11$Time<-NA
Animals_lsmeans_11$Time[Animals_lsmeans_11$timefactor==1]<-"Baseline"
Animals_lsmeans_11$Time[Animals_lsmeans_11$timefactor==2]<-"Follow-up 1"
Animals_lsmeans_11$Time[Animals_lsmeans_11$timefactor==3]<-"Follow-up 2"
ggplot(Animals_lsmeans_11, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "Animal Fluency (words)", title = "Animal Fluency Score from Baseline to FU2 by Pandemic status") +
theme_bw()
All models use normalized cognitive scores. Each model is adjusted for education, ethnicity, income level, baseline BMI, baseline CESD-10 score, smoking status, relationship status at baseline, living status at baseline, diagnosis of anxiety or mood disorder at baseline, number of chronic conditions at baseline, baseline PASE score, and baseline cognitive performance
modelRVLT_imm_adj11<- lmer(RVLT_Immediate_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
(1|ID), data= truncated.data_long_2)
summary(modelRVLT_imm_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Immediate_Normed ~ timefactor * Pandemic * Age_sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + RVLT_Immediate_Normedbaseline +
## (1 | ID)
## Data: truncated.data_long_2
##
## REML criterion at convergence: 70675.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6842 -0.5711 -0.0390 0.5248 3.8767
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 3.969 1.992
## Residual 7.501 2.739
## Number of obs: 13535, groups: ID, 6973
##
## Fixed effects:
## Estimate
## (Intercept) 6.419e+00
## timefactor2 4.083e-01
## PandemicFU2 data collected before COVID-19 6.392e-02
## Age_sexFemales 65+ -1.020e-01
## Age_sexMales 45-64 -6.870e-01
## Age_sexMales 65+ -7.001e-01
## EducationHigh School Diploma 2.785e-01
## EducationLess than High School Diploma 4.132e-01
## EducationSome College 1.261e-01
## EthnicityWhite 6.143e-01
## IncomeLevel>$150k 4.011e-01
## IncomeLevel$100-150k 2.917e-01
## IncomeLevel$20-50k 1.548e-01
## IncomeLevel$50-100k 4.087e-01
## BMI -1.458e-02
## CESD.10baseline -2.201e-02
## SmokingStatusFormer Smoker 2.137e-01
## SmokingStatusNever Smoked 3.353e-01
## SmokingStatusOccasional Smoker 3.579e-02
## RelationshipstatusMarried 3.378e-01
## RelationshipstatusSeparated 3.182e-01
## RelationshipstatusSingle 2.442e-01
## RelationshipstatusWidowed -1.827e-02
## LivingstatusAssisted Living -9.862e-01
## LivingstatusHouse -3.390e-02
## LivingstatusOther -5.054e-01
## AnxietyYes 6.142e-02
## MoodDisordYes -1.045e-01
## Chronicconditions -3.015e-02
## PASE_TOTALbaseline 2.510e-03
## RVLT_Immediate_Normedbaseline 3.543e-01
## timefactor2:PandemicFU2 data collected before COVID-19 -1.805e-01
## timefactor2:Age_sexFemales 65+ -4.964e-01
## timefactor2:Age_sexMales 45-64 9.972e-02
## timefactor2:Age_sexMales 65+ -4.198e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.279e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.283e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 6.785e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 9.935e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.135e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 4.156e-01
## Std. Error
## (Intercept) 3.758e-01
## timefactor2 1.204e-01
## PandemicFU2 data collected before COVID-19 1.428e-01
## Age_sexFemales 65+ 1.897e-01
## Age_sexMales 45-64 1.386e-01
## Age_sexMales 65+ 1.761e-01
## EducationHigh School Diploma 1.064e-01
## EducationLess than High School Diploma 1.514e-01
## EducationSome College 1.319e-01
## EthnicityWhite 2.106e-01
## IncomeLevel>$150k 1.948e-01
## IncomeLevel$100-150k 1.578e-01
## IncomeLevel$20-50k 1.067e-01
## IncomeLevel$50-100k 1.147e-01
## BMI 7.161e-03
## CESD.10baseline 8.549e-03
## SmokingStatusFormer Smoker 1.449e-01
## SmokingStatusNever Smoked 1.510e-01
## SmokingStatusOccasional Smoker 2.852e-01
## RelationshipstatusMarried 1.247e-01
## RelationshipstatusSeparated 2.416e-01
## RelationshipstatusSingle 1.689e-01
## RelationshipstatusWidowed 1.729e-01
## LivingstatusAssisted Living 5.251e-01
## LivingstatusHouse 1.138e-01
## LivingstatusOther 4.213e-01
## AnxietyYes 1.486e-01
## MoodDisordYes 1.075e-01
## Chronicconditions 1.740e-02
## PASE_TOTALbaseline 4.884e-04
## RVLT_Immediate_Normedbaseline 8.982e-03
## timefactor2:PandemicFU2 data collected before COVID-19 1.648e-01
## timefactor2:Age_sexFemales 65+ 2.090e-01
## timefactor2:Age_sexMales 45-64 1.564e-01
## timefactor2:Age_sexMales 65+ 1.998e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.489e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.073e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.446e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.878e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.387e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.832e-01
## df
## (Intercept) 7.220e+03
## timefactor2 6.860e+03
## PandemicFU2 data collected before COVID-19 1.214e+04
## Age_sexFemales 65+ 1.165e+04
## Age_sexMales 45-64 1.191e+04
## Age_sexMales 65+ 1.190e+04
## EducationHigh School Diploma 6.914e+03
## EducationLess than High School Diploma 7.040e+03
## EducationSome College 6.834e+03
## EthnicityWhite 6.836e+03
## IncomeLevel>$150k 6.913e+03
## IncomeLevel$100-150k 6.869e+03
## IncomeLevel$20-50k 6.920e+03
## IncomeLevel$50-100k 6.917e+03
## BMI 6.884e+03
## CESD.10baseline 6.904e+03
## SmokingStatusFormer Smoker 6.933e+03
## SmokingStatusNever Smoked 6.934e+03
## SmokingStatusOccasional Smoker 6.804e+03
## RelationshipstatusMarried 6.923e+03
## RelationshipstatusSeparated 6.985e+03
## RelationshipstatusSingle 6.923e+03
## RelationshipstatusWidowed 6.927e+03
## LivingstatusAssisted Living 6.926e+03
## LivingstatusHouse 6.919e+03
## LivingstatusOther 6.791e+03
## AnxietyYes 6.889e+03
## MoodDisordYes 6.906e+03
## Chronicconditions 6.906e+03
## PASE_TOTALbaseline 6.885e+03
## RVLT_Immediate_Normedbaseline 6.921e+03
## timefactor2:PandemicFU2 data collected before COVID-19 6.790e+03
## timefactor2:Age_sexFemales 65+ 6.854e+03
## timefactor2:Age_sexMales 45-64 6.820e+03
## timefactor2:Age_sexMales 65+ 6.875e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.214e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.215e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.216e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 6.814e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 6.749e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 6.808e+03
## t value
## (Intercept) 17.081
## timefactor2 3.390
## PandemicFU2 data collected before COVID-19 0.448
## Age_sexFemales 65+ -0.538
## Age_sexMales 45-64 -4.958
## Age_sexMales 65+ -3.976
## EducationHigh School Diploma 2.617
## EducationLess than High School Diploma 2.730
## EducationSome College 0.956
## EthnicityWhite 2.917
## IncomeLevel>$150k 2.059
## IncomeLevel$100-150k 1.848
## IncomeLevel$20-50k 1.452
## IncomeLevel$50-100k 3.562
## BMI -2.036
## CESD.10baseline -2.574
## SmokingStatusFormer Smoker 1.474
## SmokingStatusNever Smoked 2.221
## SmokingStatusOccasional Smoker 0.126
## RelationshipstatusMarried 2.709
## RelationshipstatusSeparated 1.317
## RelationshipstatusSingle 1.446
## RelationshipstatusWidowed -0.106
## LivingstatusAssisted Living -1.878
## LivingstatusHouse -0.298
## LivingstatusOther -1.200
## AnxietyYes 0.413
## MoodDisordYes -0.972
## Chronicconditions -1.733
## PASE_TOTALbaseline 5.140
## RVLT_Immediate_Normedbaseline 39.448
## timefactor2:PandemicFU2 data collected before COVID-19 -1.096
## timefactor2:Age_sexFemales 65+ -2.375
## timefactor2:Age_sexMales 45-64 0.638
## timefactor2:Age_sexMales 65+ -2.101
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.514
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.619
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.277
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.345
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.894
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.467
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 0.000703
## PandemicFU2 data collected before COVID-19 0.654369
## Age_sexFemales 65+ 0.590623
## Age_sexMales 45-64 7.21e-07
## Age_sexMales 65+ 7.06e-05
## EducationHigh School Diploma 0.008883
## EducationLess than High School Diploma 0.006347
## EducationSome College 0.339107
## EthnicityWhite 0.003551
## IncomeLevel>$150k 0.039571
## IncomeLevel$100-150k 0.064584
## IncomeLevel$20-50k 0.146668
## IncomeLevel$50-100k 0.000371
## BMI 0.041772
## CESD.10baseline 0.010075
## SmokingStatusFormer Smoker 0.140424
## SmokingStatusNever Smoked 0.026361
## SmokingStatusOccasional Smoker 0.900129
## RelationshipstatusMarried 0.006756
## RelationshipstatusSeparated 0.187821
## RelationshipstatusSingle 0.148248
## RelationshipstatusWidowed 0.915818
## LivingstatusAssisted Living 0.060430
## LivingstatusHouse 0.765724
## LivingstatusOther 0.230252
## AnxietyYes 0.679422
## MoodDisordYes 0.330894
## Chronicconditions 0.083121
## PASE_TOTALbaseline 2.83e-07
## RVLT_Immediate_Normedbaseline < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19 0.273332
## timefactor2:Age_sexFemales 65+ 0.017580
## timefactor2:Age_sexMales 45-64 0.523815
## timefactor2:Age_sexMales 65+ 0.035651
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.607355
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.535879
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.781483
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.729926
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.371132
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.142294
##
## (Intercept) ***
## timefactor2 ***
## PandemicFU2 data collected before COVID-19
## Age_sexFemales 65+
## Age_sexMales 45-64 ***
## Age_sexMales 65+ ***
## EducationHigh School Diploma **
## EducationLess than High School Diploma **
## EducationSome College
## EthnicityWhite **
## IncomeLevel>$150k *
## IncomeLevel$100-150k .
## IncomeLevel$20-50k
## IncomeLevel$50-100k ***
## BMI *
## CESD.10baseline *
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked *
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried **
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed
## LivingstatusAssisted Living .
## LivingstatusHouse
## LivingstatusOther
## AnxietyYes
## MoodDisordYes
## Chronicconditions .
## PASE_TOTALbaseline ***
## RVLT_Immediate_Normedbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19
## timefactor2:Age_sexFemales 65+ *
## timefactor2:Age_sexMales 45-64
## timefactor2:Age_sexMales 65+ *
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Significantly lower RVLT immediate for males 65+
lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.647131 0.2484999 Inf 10.160081 11.13418
## FU2 data collected before COVID-19 10.711050 0.2479757 Inf 10.225026 11.19707
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.055400 0.2492345 Inf 10.566909 11.54389
## FU2 data collected before COVID-19 10.938824 0.2479555 Inf 10.452840 11.42481
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.545094 0.2702757 Inf 10.015363 11.07482
## FU2 data collected before COVID-19 10.736920 0.2710850 Inf 10.205603 11.26824
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.456998 0.2718546 Inf 9.924173 10.98982
## FU2 data collected before COVID-19 10.567679 0.2715214 Inf 10.035507 11.09985
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.960145 0.2406574 Inf 9.488465 10.43182
## FU2 data collected before COVID-19 10.152377 0.2563105 Inf 9.650017 10.65474
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.468137 0.2408704 Inf 9.996039 10.94023
## FU2 data collected before COVID-19 10.693349 0.2562374 Inf 10.191133 11.19557
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.947081 0.2681867 Inf 9.421445 10.47272
## FU2 data collected before COVID-19 10.078848 0.2697431 Inf 9.550161 10.60753
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.935507 0.2699631 Inf 9.406389 10.46462
## FU2 data collected before COVID-19 10.302399 0.2698663 Inf 9.773471 10.83133
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0639181 0.1427662 Inf -0.448 0.6544
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.1165756 0.1442439 Inf 0.808 0.4190
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1918261 0.2043721 Inf -0.939 0.3479
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1106807 0.2071280 Inf -0.534 0.5931
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1922317 0.1508459 Inf -1.274 0.2025
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2252127 0.1508996 Inf -1.492 0.1356
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1317663 0.1988721 Inf -0.663 0.5076
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3668923 0.2012933 Inf -1.823 0.0684
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_imm_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0639181 0.1427662 Inf -0.3437347 0.2158984
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.1165756 0.1442439 Inf -0.1661373 0.3992886
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1918261 0.2043721 Inf -0.5923880 0.2087358
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1106807 0.2071280 Inf -0.5166441 0.2952828
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1922317 0.1508459 Inf -0.4878843 0.1034209
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2252127 0.1508996 Inf -0.5209705 0.0705452
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1317663 0.1988721 Inf -0.5215485 0.2580159
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3668923 0.2012933 Inf -0.7614198 0.0276352
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTimmediate_lsmeans_adj11 <- summary(lsmeans(modelRVLT_imm_adj11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13535' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13535)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTimmediate_lsmeans_adj11$Time<-NA
RVLTimmediate_lsmeans_adj11$Time[RVLTimmediate_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
RVLTimmediate_lsmeans_adj11$Time[RVLTimmediate_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(RVLTimmediate_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
labs(x = "Time", y = "RVLT Immediate Normalized Score", title = "RVLT Immediate Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
modelRVLT_del_adj11<- lmer(RVLT_Delayed_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
(1|ID), data= truncated.data_long_2)
summary(modelRVLT_del_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: RVLT_Delayed_Normed ~ timefactor * Pandemic * Age_sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + RVLT_Delayed_Normedbaseline +
## (1 | ID)
## Data: truncated.data_long_2
##
## REML criterion at convergence: 69356.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9609 -0.5522 -0.0351 0.5078 3.9656
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 4.105 2.026
## Residual 6.899 2.627
## Number of obs: 13415, groups: ID, 6961
##
## Fixed effects:
## Estimate
## (Intercept) 6.148e+00
## timefactor2 8.000e-01
## PandemicFU2 data collected before COVID-19 1.582e-01
## Age_sexFemales 65+ 1.129e-01
## Age_sexMales 45-64 -4.896e-01
## Age_sexMales 65+ -4.101e-01
## EducationHigh School Diploma 2.166e-01
## EducationLess than High School Diploma 2.815e-01
## EducationSome College 3.141e-01
## EthnicityWhite 6.586e-01
## IncomeLevel>$150k 2.668e-01
## IncomeLevel$100-150k 1.725e-01
## IncomeLevel$20-50k 1.689e-01
## IncomeLevel$50-100k 4.474e-01
## BMI -1.715e-02
## CESD.10baseline -1.956e-02
## SmokingStatusFormer Smoker 1.046e-01
## SmokingStatusNever Smoked 3.164e-01
## SmokingStatusOccasional Smoker 2.048e-01
## RelationshipstatusMarried 5.045e-02
## RelationshipstatusSeparated -9.984e-02
## RelationshipstatusSingle -3.306e-02
## RelationshipstatusWidowed -1.919e-01
## LivingstatusAssisted Living -1.296e+00
## LivingstatusHouse 5.338e-02
## LivingstatusOther -1.715e-01
## AnxietyYes 5.713e-02
## MoodDisordYes -1.087e-01
## Chronicconditions -3.436e-02
## PASE_TOTALbaseline 2.659e-03
## RVLT_Delayed_Normedbaseline 3.942e-01
## timefactor2:PandemicFU2 data collected before COVID-19 -4.264e-01
## timefactor2:Age_sexFemales 65+ -6.196e-01
## timefactor2:Age_sexMales 45-64 -8.355e-02
## timefactor2:Age_sexMales 65+ -5.885e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.142e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 9.945e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 8.237e-02
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 3.722e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.475e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3.678e-01
## Std. Error
## (Intercept) 3.753e-01
## timefactor2 1.158e-01
## PandemicFU2 data collected before COVID-19 1.401e-01
## Age_sexFemales 65+ 1.865e-01
## Age_sexMales 45-64 1.363e-01
## Age_sexMales 65+ 1.738e-01
## EducationHigh School Diploma 1.056e-01
## EducationLess than High School Diploma 1.507e-01
## EducationSome College 1.306e-01
## EthnicityWhite 2.103e-01
## IncomeLevel>$150k 1.930e-01
## IncomeLevel$100-150k 1.565e-01
## IncomeLevel$20-50k 1.059e-01
## IncomeLevel$50-100k 1.138e-01
## BMI 7.097e-03
## CESD.10baseline 8.490e-03
## SmokingStatusFormer Smoker 1.435e-01
## SmokingStatusNever Smoked 1.495e-01
## SmokingStatusOccasional Smoker 2.832e-01
## RelationshipstatusMarried 1.239e-01
## RelationshipstatusSeparated 2.399e-01
## RelationshipstatusSingle 1.675e-01
## RelationshipstatusWidowed 1.717e-01
## LivingstatusAssisted Living 5.220e-01
## LivingstatusHouse 1.130e-01
## LivingstatusOther 4.194e-01
## AnxietyYes 1.475e-01
## MoodDisordYes 1.065e-01
## Chronicconditions 1.725e-02
## PASE_TOTALbaseline 4.848e-04
## RVLT_Delayed_Normedbaseline 9.104e-03
## timefactor2:PandemicFU2 data collected before COVID-19 1.584e-01
## timefactor2:Age_sexFemales 65+ 2.021e-01
## timefactor2:Age_sexMales 45-64 1.506e-01
## timefactor2:Age_sexMales 65+ 1.935e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.445e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.036e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.411e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.779e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.298e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.740e-01
## df
## (Intercept) 7.230e+03
## timefactor2 6.751e+03
## PandemicFU2 data collected before COVID-19 1.187e+04
## Age_sexFemales 65+ 1.137e+04
## Age_sexMales 45-64 1.167e+04
## Age_sexMales 65+ 1.169e+04
## EducationHigh School Diploma 6.899e+03
## EducationLess than High School Diploma 7.063e+03
## EducationSome College 6.797e+03
## EthnicityWhite 6.920e+03
## IncomeLevel>$150k 6.885e+03
## IncomeLevel$100-150k 6.852e+03
## IncomeLevel$20-50k 6.911e+03
## IncomeLevel$50-100k 6.894e+03
## BMI 6.844e+03
## CESD.10baseline 6.906e+03
## SmokingStatusFormer Smoker 6.898e+03
## SmokingStatusNever Smoked 6.899e+03
## SmokingStatusOccasional Smoker 6.825e+03
## RelationshipstatusMarried 6.886e+03
## RelationshipstatusSeparated 6.994e+03
## RelationshipstatusSingle 6.878e+03
## RelationshipstatusWidowed 6.904e+03
## LivingstatusAssisted Living 6.965e+03
## LivingstatusHouse 6.916e+03
## LivingstatusOther 6.852e+03
## AnxietyYes 6.865e+03
## MoodDisordYes 6.871e+03
## Chronicconditions 6.872e+03
## PASE_TOTALbaseline 6.882e+03
## RVLT_Delayed_Normedbaseline 6.888e+03
## timefactor2:PandemicFU2 data collected before COVID-19 6.685e+03
## timefactor2:Age_sexFemales 65+ 6.798e+03
## timefactor2:Age_sexMales 45-64 6.731e+03
## timefactor2:Age_sexMales 65+ 6.837e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.188e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.189e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.194e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 6.749e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 6.661e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 6.760e+03
## t value
## (Intercept) 16.381
## timefactor2 6.908
## PandemicFU2 data collected before COVID-19 1.129
## Age_sexFemales 65+ 0.605
## Age_sexMales 45-64 -3.592
## Age_sexMales 65+ -2.360
## EducationHigh School Diploma 2.051
## EducationLess than High School Diploma 1.868
## EducationSome College 2.404
## EthnicityWhite 3.131
## IncomeLevel>$150k 1.382
## IncomeLevel$100-150k 1.102
## IncomeLevel$20-50k 1.595
## IncomeLevel$50-100k 3.931
## BMI -2.416
## CESD.10baseline -2.304
## SmokingStatusFormer Smoker 0.729
## SmokingStatusNever Smoked 2.117
## SmokingStatusOccasional Smoker 0.723
## RelationshipstatusMarried 0.407
## RelationshipstatusSeparated -0.416
## RelationshipstatusSingle -0.197
## RelationshipstatusWidowed -1.118
## LivingstatusAssisted Living -2.483
## LivingstatusHouse 0.472
## LivingstatusOther -0.409
## AnxietyYes 0.387
## MoodDisordYes -1.021
## Chronicconditions -1.992
## PASE_TOTALbaseline 5.484
## RVLT_Delayed_Normedbaseline 43.300
## timefactor2:PandemicFU2 data collected before COVID-19 -2.692
## timefactor2:Age_sexFemales 65+ -3.065
## timefactor2:Age_sexMales 45-64 -0.555
## timefactor2:Age_sexMales 65+ -3.041
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.876
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.489
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.342
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.339
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.077
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.342
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 5.37e-12
## PandemicFU2 data collected before COVID-19 0.25897
## Age_sexFemales 65+ 0.54491
## Age_sexMales 45-64 0.00033
## Age_sexMales 65+ 0.01828
## EducationHigh School Diploma 0.04030
## EducationLess than High School Diploma 0.06180
## EducationSome College 0.01624
## EthnicityWhite 0.00175
## IncomeLevel>$150k 0.16691
## IncomeLevel$100-150k 0.27060
## IncomeLevel$20-50k 0.11068
## IncomeLevel$50-100k 8.55e-05
## BMI 0.01572
## CESD.10baseline 0.02124
## SmokingStatusFormer Smoker 0.46600
## SmokingStatusNever Smoked 0.03432
## SmokingStatusOccasional Smoker 0.46959
## RelationshipstatusMarried 0.68378
## RelationshipstatusSeparated 0.67724
## RelationshipstatusSingle 0.84355
## RelationshipstatusWidowed 0.26371
## LivingstatusAssisted Living 0.01304
## LivingstatusHouse 0.63660
## LivingstatusOther 0.68268
## AnxietyYes 0.69850
## MoodDisordYes 0.30728
## Chronicconditions 0.04644
## PASE_TOTALbaseline 4.31e-08
## RVLT_Delayed_Normedbaseline < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19 0.00712
## timefactor2:Age_sexFemales 65+ 0.00218
## timefactor2:Age_sexMales 45-64 0.57920
## timefactor2:Age_sexMales 65+ 0.00237
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.38102
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.62520
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.73264
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.18053
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.28139
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.17961
##
## (Intercept) ***
## timefactor2 ***
## PandemicFU2 data collected before COVID-19
## Age_sexFemales 65+
## Age_sexMales 45-64 ***
## Age_sexMales 65+ *
## EducationHigh School Diploma *
## EducationLess than High School Diploma .
## EducationSome College *
## EthnicityWhite **
## IncomeLevel>$150k
## IncomeLevel$100-150k
## IncomeLevel$20-50k
## IncomeLevel$50-100k ***
## BMI *
## CESD.10baseline *
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked *
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed
## LivingstatusAssisted Living *
## LivingstatusHouse
## LivingstatusOther
## AnxietyYes
## MoodDisordYes
## Chronicconditions *
## PASE_TOTALbaseline ***
## RVLT_Delayed_Normedbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19 **
## timefactor2:Age_sexFemales 65+ **
## timefactor2:Age_sexMales 45-64
## timefactor2:Age_sexMales 65+ **
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.63930 0.2467899 Inf 10.155596 11.12299
## FU2 data collected before COVID-19 10.79750 0.2460794 Inf 10.315189 11.27980
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 11.43926 0.2472123 Inf 10.954731 11.92379
## FU2 data collected before COVID-19 11.17108 0.2461349 Inf 10.688665 11.65350
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.75218 0.2677506 Inf 10.227402 11.27697
## FU2 data collected before COVID-19 11.12462 0.2688596 Inf 10.597662 11.65157
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.93256 0.2703290 Inf 10.402729 11.46240
## FU2 data collected before COVID-19 11.25081 0.2692886 Inf 10.723019 11.77861
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.14967 0.2390858 Inf 9.681072 10.61827
## FU2 data collected before COVID-19 10.40732 0.2541979 Inf 9.909106 10.90554
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.86609 0.2391310 Inf 10.397399 11.33478
## FU2 data collected before COVID-19 10.94491 0.2542058 Inf 10.446673 11.44314
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.22915 0.2662749 Inf 9.707259 10.75104
## FU2 data collected before COVID-19 10.46972 0.2679588 Inf 9.944529 10.99491
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.44062 0.2679346 Inf 9.915474 10.96576
## FU2 data collected before COVID-19 10.62261 0.2676523 Inf 10.098019 11.14720
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1582006 0.1401405 Inf -1.129 0.2590
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.2681783 0.1413314 Inf 1.898 0.0578
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3724328 0.2008420 Inf -1.854 0.0637
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3182511 0.2043285 Inf -1.558 0.1193
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2576531 0.1482219 Inf -1.738 0.0822
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0788205 0.1482675 Inf -0.532 0.5950
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.2405706 0.1964722 Inf -1.224 0.2208
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1819915 0.1984004 Inf -0.917 0.3590
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelRVLT_del_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1582006 0.1401405 Inf -0.4328709 0.1164697
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.2681783 0.1413314 Inf -0.0088261 0.5451828
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3724328 0.2008420 Inf -0.7660758 0.0212102
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3182511 0.2043285 Inf -0.7187277 0.0822254
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2576531 0.1482219 Inf -0.5481626 0.0328564
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0788205 0.1482675 Inf -0.3694196 0.2117785
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.2405706 0.1964722 Inf -0.6256491 0.1445079
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1819915 0.1984004 Inf -0.5708491 0.2068660
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
RVLTdelayed_lsmeans_adj11 <- summary(lsmeans(modelRVLT_del_adj11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13415' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13415)' or larger];
## but be warned that this may result in large computation time and memory use.
RVLTdelayed_lsmeans_adj11$Time<-NA
RVLTdelayed_lsmeans_adj11$Time[RVLTdelayed_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
RVLTdelayed_lsmeans_adj11$Time[RVLTdelayed_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(RVLTdelayed_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
labs(x = "Time", y = "RVLT Delayed Normalized Score", title = "RVLT Delayed Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
modelMAT_adj11<- lmer(MAT_Normed~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + MAT_Normedbaseline +
(1|ID), data= truncated.data_long_2)
summary(modelMAT_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## MAT_Normed ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +
## IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
## Livingstatus + Anxiety + MoodDisord + Chronicconditions +
## PASE_TOTALbaseline + MAT_Normedbaseline + (1 | ID)
## Data: truncated.data_long_2
##
## REML criterion at convergence: 65712.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2923 -0.5209 -0.0531 0.3969 4.4907
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.583 1.607
## Residual 8.337 2.887
## Number of obs: 12616, groups: ID, 6874
##
## Fixed effects:
## Estimate
## (Intercept) 8.347e+00
## timefactor2 -3.248e+00
## PandemicFU2 data collected before COVID-19 -1.257e+00
## Age_sexFemales 65+ -6.694e-01
## Age_sexMales 45-64 -3.233e+00
## Age_sexMales 65+ -3.301e+00
## EducationHigh School Diploma -5.268e-02
## EducationLess than High School Diploma -1.556e-01
## EducationSome College -4.562e-02
## EthnicityWhite 8.511e-01
## IncomeLevel>$150k 1.278e-01
## IncomeLevel$100-150k 1.582e-01
## IncomeLevel$20-50k 1.493e-01
## IncomeLevel$50-100k 1.341e-01
## BMI -1.124e-02
## CESD.10baseline -2.172e-02
## SmokingStatusFormer Smoker 1.522e-02
## SmokingStatusNever Smoked -6.242e-02
## SmokingStatusOccasional Smoker 1.890e-01
## RelationshipstatusMarried -2.531e-02
## RelationshipstatusSeparated -8.381e-02
## RelationshipstatusSingle 5.368e-01
## RelationshipstatusWidowed -2.280e-01
## LivingstatusAssisted Living -1.794e-01
## LivingstatusHouse -1.703e-01
## LivingstatusOther -2.286e-01
## AnxietyYes 7.590e-02
## MoodDisordYes 1.099e-01
## Chronicconditions -3.439e-02
## PASE_TOTALbaseline -3.153e-04
## MAT_Normedbaseline 4.417e-01
## timefactor2:PandemicFU2 data collected before COVID-19 1.418e+00
## timefactor2:Age_sexFemales 65+ 4.689e-01
## timefactor2:Age_sexMales 45-64 3.291e+00
## timefactor2:Age_sexMales 65+ 2.884e+00
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 3.934e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.309e+00
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.557e+00
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.594e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.480e+00
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.321e+00
## Std. Error
## (Intercept) 3.681e-01
## timefactor2 1.299e-01
## PandemicFU2 data collected before COVID-19 1.429e-01
## Age_sexFemales 65+ 1.934e-01
## Age_sexMales 45-64 1.388e-01
## Age_sexMales 65+ 1.799e-01
## EducationHigh School Diploma 1.027e-01
## EducationLess than High School Diploma 1.486e-01
## EducationSome College 1.264e-01
## EthnicityWhite 2.052e-01
## IncomeLevel>$150k 1.873e-01
## IncomeLevel$100-150k 1.519e-01
## IncomeLevel$20-50k 1.033e-01
## IncomeLevel$50-100k 1.110e-01
## BMI 6.878e-03
## CESD.10baseline 8.249e-03
## SmokingStatusFormer Smoker 1.389e-01
## SmokingStatusNever Smoked 1.447e-01
## SmokingStatusOccasional Smoker 2.741e-01
## RelationshipstatusMarried 1.205e-01
## RelationshipstatusSeparated 2.317e-01
## RelationshipstatusSingle 1.629e-01
## RelationshipstatusWidowed 1.680e-01
## LivingstatusAssisted Living 5.081e-01
## LivingstatusHouse 1.102e-01
## LivingstatusOther 4.105e-01
## AnxietyYes 1.428e-01
## MoodDisordYes 1.031e-01
## Chronicconditions 1.682e-02
## PASE_TOTALbaseline 4.690e-04
## MAT_Normedbaseline 9.548e-03
## timefactor2:PandemicFU2 data collected before COVID-19 1.779e-01
## timefactor2:Age_sexFemales 65+ 2.328e-01
## timefactor2:Age_sexMales 45-64 1.694e-01
## timefactor2:Age_sexMales 65+ 2.231e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.541e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.089e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.503e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 3.187e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.596e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3.147e-01
## df
## (Intercept) 7.098e+03
## timefactor2 6.317e+03
## PandemicFU2 data collected before COVID-19 1.208e+04
## Age_sexFemales 65+ 1.183e+04
## Age_sexMales 45-64 1.192e+04
## Age_sexMales 65+ 1.195e+04
## EducationHigh School Diploma 6.637e+03
## EducationLess than High School Diploma 6.853e+03
## EducationSome College 6.555e+03
## EthnicityWhite 6.723e+03
## IncomeLevel>$150k 6.613e+03
## IncomeLevel$100-150k 6.602e+03
## IncomeLevel$20-50k 6.689e+03
## IncomeLevel$50-100k 6.649e+03
## BMI 6.570e+03
## CESD.10baseline 6.615e+03
## SmokingStatusFormer Smoker 6.575e+03
## SmokingStatusNever Smoked 6.578e+03
## SmokingStatusOccasional Smoker 6.441e+03
## RelationshipstatusMarried 6.699e+03
## RelationshipstatusSeparated 6.693e+03
## RelationshipstatusSingle 6.672e+03
## RelationshipstatusWidowed 6.758e+03
## LivingstatusAssisted Living 6.556e+03
## LivingstatusHouse 6.708e+03
## LivingstatusOther 6.680e+03
## AnxietyYes 6.615e+03
## MoodDisordYes 6.583e+03
## Chronicconditions 6.654e+03
## PASE_TOTALbaseline 6.589e+03
## MAT_Normedbaseline 6.684e+03
## timefactor2:PandemicFU2 data collected before COVID-19 6.249e+03
## timefactor2:Age_sexFemales 65+ 6.637e+03
## timefactor2:Age_sexMales 45-64 6.312e+03
## timefactor2:Age_sexMales 65+ 6.597e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.214e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.211e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.214e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 6.541e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 6.290e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 6.501e+03
## t value
## (Intercept) 22.678
## timefactor2 -25.001
## PandemicFU2 data collected before COVID-19 -8.795
## Age_sexFemales 65+ -3.462
## Age_sexMales 45-64 -23.300
## Age_sexMales 65+ -18.352
## EducationHigh School Diploma -0.513
## EducationLess than High School Diploma -1.047
## EducationSome College -0.361
## EthnicityWhite 4.147
## IncomeLevel>$150k 0.682
## IncomeLevel$100-150k 1.041
## IncomeLevel$20-50k 1.446
## IncomeLevel$50-100k 1.208
## BMI -1.634
## CESD.10baseline -2.634
## SmokingStatusFormer Smoker 0.110
## SmokingStatusNever Smoked -0.431
## SmokingStatusOccasional Smoker 0.690
## RelationshipstatusMarried -0.210
## RelationshipstatusSeparated -0.362
## RelationshipstatusSingle 3.296
## RelationshipstatusWidowed -1.358
## LivingstatusAssisted Living -0.353
## LivingstatusHouse -1.546
## LivingstatusOther -0.557
## AnxietyYes 0.532
## MoodDisordYes 1.066
## Chronicconditions -2.045
## PASE_TOTALbaseline -0.672
## MAT_Normedbaseline 46.264
## timefactor2:PandemicFU2 data collected before COVID-19 7.974
## timefactor2:Age_sexFemales 65+ 2.014
## timefactor2:Age_sexMales 45-64 19.426
## timefactor2:Age_sexMales 65+ 12.930
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.155
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 6.268
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 6.221
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.500
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -5.699
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -4.198
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 < 2e-16
## PandemicFU2 data collected before COVID-19 < 2e-16
## Age_sexFemales 65+ 0.000539
## Age_sexMales 45-64 < 2e-16
## Age_sexMales 65+ < 2e-16
## EducationHigh School Diploma 0.608106
## EducationLess than High School Diploma 0.295088
## EducationSome College 0.718135
## EthnicityWhite 3.41e-05
## IncomeLevel>$150k 0.495147
## IncomeLevel$100-150k 0.297709
## IncomeLevel$20-50k 0.148176
## IncomeLevel$50-100k 0.227092
## BMI 0.102390
## CESD.10baseline 0.008468
## SmokingStatusFormer Smoker 0.912753
## SmokingStatusNever Smoked 0.666243
## SmokingStatusOccasional Smoker 0.490462
## RelationshipstatusMarried 0.833712
## RelationshipstatusSeparated 0.717577
## RelationshipstatusSingle 0.000986
## RelationshipstatusWidowed 0.174660
## LivingstatusAssisted Living 0.724074
## LivingstatusHouse 0.122258
## LivingstatusOther 0.577619
## AnxietyYes 0.595041
## MoodDisordYes 0.286426
## Chronicconditions 0.040894
## PASE_TOTALbaseline 0.501426
## MAT_Normedbaseline < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19 1.82e-15
## timefactor2:Age_sexFemales 65+ 0.044036
## timefactor2:Age_sexMales 45-64 < 2e-16
## timefactor2:Age_sexMales 65+ < 2e-16
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.876985
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 3.78e-10
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 5.10e-10
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.616990
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.26e-08
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.73e-05
##
## (Intercept) ***
## timefactor2 ***
## PandemicFU2 data collected before COVID-19 ***
## Age_sexFemales 65+ ***
## Age_sexMales 45-64 ***
## Age_sexMales 65+ ***
## EducationHigh School Diploma
## EducationLess than High School Diploma
## EducationSome College
## EthnicityWhite ***
## IncomeLevel>$150k
## IncomeLevel$100-150k
## IncomeLevel$20-50k
## IncomeLevel$50-100k
## BMI
## CESD.10baseline **
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle ***
## RelationshipstatusWidowed
## LivingstatusAssisted Living
## LivingstatusHouse
## LivingstatusOther
## AnxietyYes
## MoodDisordYes
## Chronicconditions *
## PASE_TOTALbaseline
## MAT_Normedbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19 ***
## timefactor2:Age_sexFemales 65+ *
## timefactor2:Age_sexMales 45-64 ***
## timefactor2:Age_sexMales 65+ ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 ***
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ ***
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 ***
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 12.744170 0.2424141 Inf 12.269047 13.219293
## FU2 data collected before COVID-19 11.487398 0.2416037 Inf 11.013864 11.960933
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.496412 0.2425075 Inf 9.021106 9.971718
## FU2 data collected before COVID-19 9.658130 0.2411418 Inf 9.185501 10.130759
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 12.074787 0.2675273 Inf 11.550444 12.599131
## FU2 data collected before COVID-19 10.857355 0.2664621 Inf 10.335098 11.379611
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.295957 0.2677245 Inf 8.771226 9.820687
## FU2 data collected before COVID-19 9.656410 0.2664143 Inf 9.134247 10.178572
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.511111 0.2344499 Inf 9.051598 9.970625
## FU2 data collected before COVID-19 9.563801 0.2512334 Inf 9.071392 10.056209
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.554576 0.2340630 Inf 9.095821 10.013331
## FU2 data collected before COVID-19 9.546113 0.2501548 Inf 9.055819 10.036408
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.443127 0.2654002 Inf 8.922952 9.963302
## FU2 data collected before COVID-19 9.743585 0.2667682 Inf 9.220729 10.266441
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.079691 0.2675412 Inf 8.555320 9.604062
## FU2 data collected before COVID-19 9.477504 0.2657853 Inf 8.956574 9.998433
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 1.2567714 0.1428988 Inf 8.795 <.0001
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.1617181 0.1430028 Inf -1.131 0.2581
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 1.2174328 0.2105296 Inf 5.783 <.0001
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3604533 0.2116721 Inf -1.703 0.0886
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.0526895 0.1527831 Inf -0.345 0.7302
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.0084627 0.1505752 Inf 0.056 0.9552
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3004582 0.2057308 Inf -1.460 0.1442
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.3978132 0.2075758 Inf -1.916 0.0553
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 1.2567714 0.1428988 Inf 0.9766950 1.5368479
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.1617181 0.1430028 Inf -0.4419984 0.1185622
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 1.2174328 0.2105296 Inf 0.8048023 1.6300633
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3604533 0.2116721 Inf -0.7753229 0.0544164
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.0526895 0.1527831 Inf -0.3521388 0.2467598
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.0084627 0.1505752 Inf -0.2866593 0.3035847
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3004582 0.2057308 Inf -0.7036832 0.1027668
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.3978132 0.2075758 Inf -0.8046542 0.0090279
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
MAT_lsmeans_adj11 <- summary(lsmeans(modelMAT_adj11, ~Pandemic|timefactor|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 12616' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 12616)' or larger];
## but be warned that this may result in large computation time and memory use.
MAT_lsmeans_adj11$Time<-NA
MAT_lsmeans_adj11$Time[MAT_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
MAT_lsmeans_adj11$Time[MAT_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(MAT_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
labs(x = "Time", y = "MAT Normalized Score", title = "Mental Alteration Test Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
modelAnimals_adj11<- lmer(Animal_Fluency_Normed ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
(1|ID), data= truncated.data_long_2)
summary(modelAnimals_adj11)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Animal_Fluency_Normed ~ timefactor * Pandemic * Age_sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + Animal_Fluency_Normedbaseline +
## (1 | ID)
## Data: truncated.data_long_2
##
## REML criterion at convergence: 60844.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8739 -0.5573 -0.0156 0.5352 4.1659
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2.364 1.538
## Residual 3.182 1.784
## Number of obs: 13639, groups: ID, 6978
##
## Fixed effects:
## Estimate
## (Intercept) 4.161e+00
## timefactor2 3.121e-01
## PandemicFU2 data collected before COVID-19 2.369e-01
## Age_sexFemales 65+ -1.749e-01
## Age_sexMales 45-64 -3.342e-02
## Age_sexMales 65+ -1.282e-01
## EducationHigh School Diploma 1.759e-01
## EducationLess than High School Diploma 1.104e-01
## EducationSome College 1.616e-01
## EthnicityWhite 5.154e-01
## IncomeLevel>$150k -7.940e-02
## IncomeLevel$100-150k -1.919e-02
## IncomeLevel$20-50k -1.331e-01
## IncomeLevel$50-100k -6.360e-03
## BMI -8.268e-03
## CESD.10baseline -1.057e-02
## SmokingStatusFormer Smoker 1.979e-01
## SmokingStatusNever Smoked 1.870e-01
## SmokingStatusOccasional Smoker 5.156e-02
## RelationshipstatusMarried 2.691e-02
## RelationshipstatusSeparated 2.051e-01
## RelationshipstatusSingle 8.506e-02
## RelationshipstatusWidowed -8.159e-02
## LivingstatusAssisted Living -1.426e-01
## LivingstatusHouse 1.448e-01
## LivingstatusOther -2.653e-01
## AnxietyYes 1.077e-01
## MoodDisordYes 5.307e-02
## Chronicconditions -9.824e-03
## PASE_TOTALbaseline 8.361e-04
## Animal_Fluency_Normedbaseline 5.201e-01
## timefactor2:PandemicFU2 data collected before COVID-19 -1.278e-01
## timefactor2:Age_sexFemales 65+ -3.929e-01
## timefactor2:Age_sexMales 45-64 -1.890e-01
## timefactor2:Age_sexMales 65+ -5.324e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.124e-02
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -2.979e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.799e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.616e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 2.478e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3.717e-01
## Std. Error
## (Intercept) 2.708e-01
## timefactor2 7.826e-02
## PandemicFU2 data collected before COVID-19 9.902e-02
## Age_sexFemales 65+ 1.316e-01
## Age_sexMales 45-64 9.623e-02
## Age_sexMales 65+ 1.222e-01
## EducationHigh School Diploma 7.597e-02
## EducationLess than High School Diploma 1.077e-01
## EducationSome College 9.408e-02
## EthnicityWhite 1.506e-01
## IncomeLevel>$150k 1.388e-01
## IncomeLevel$100-150k 1.127e-01
## IncomeLevel$20-50k 7.606e-02
## IncomeLevel$50-100k 8.181e-02
## BMI 5.107e-03
## CESD.10baseline 6.107e-03
## SmokingStatusFormer Smoker 1.033e-01
## SmokingStatusNever Smoked 1.076e-01
## SmokingStatusOccasional Smoker 2.039e-01
## RelationshipstatusMarried 8.909e-02
## RelationshipstatusSeparated 1.721e-01
## RelationshipstatusSingle 1.205e-01
## RelationshipstatusWidowed 1.232e-01
## LivingstatusAssisted Living 3.753e-01
## LivingstatusHouse 8.122e-02
## LivingstatusOther 3.005e-01
## AnxietyYes 1.061e-01
## MoodDisordYes 7.673e-02
## Chronicconditions 1.240e-02
## PASE_TOTALbaseline 3.483e-04
## Animal_Fluency_Normedbaseline 7.852e-03
## timefactor2:PandemicFU2 data collected before COVID-19 1.070e-01
## timefactor2:Age_sexFemales 65+ 1.356e-01
## timefactor2:Age_sexMales 45-64 1.018e-01
## timefactor2:Age_sexMales 65+ 1.296e-01
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.721e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.438e-01
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.693e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.860e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.551e-01
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.834e-01
## df
## (Intercept) 7.173e+03
## timefactor2 6.869e+03
## PandemicFU2 data collected before COVID-19 1.158e+04
## Age_sexFemales 65+ 1.107e+04
## Age_sexMales 45-64 1.137e+04
## Age_sexMales 65+ 1.133e+04
## EducationHigh School Diploma 6.914e+03
## EducationLess than High School Diploma 6.990e+03
## EducationSome College 6.858e+03
## EthnicityWhite 6.852e+03
## IncomeLevel>$150k 6.912e+03
## IncomeLevel$100-150k 6.881e+03
## IncomeLevel$20-50k 6.905e+03
## IncomeLevel$50-100k 6.905e+03
## BMI 6.886e+03
## CESD.10baseline 6.919e+03
## SmokingStatusFormer Smoker 6.915e+03
## SmokingStatusNever Smoked 6.915e+03
## SmokingStatusOccasional Smoker 6.840e+03
## RelationshipstatusMarried 6.940e+03
## RelationshipstatusSeparated 6.962e+03
## RelationshipstatusSingle 6.938e+03
## RelationshipstatusWidowed 6.912e+03
## LivingstatusAssisted Living 6.965e+03
## LivingstatusHouse 6.920e+03
## LivingstatusOther 6.782e+03
## AnxietyYes 6.896e+03
## MoodDisordYes 6.912e+03
## Chronicconditions 6.886e+03
## PASE_TOTALbaseline 6.895e+03
## Animal_Fluency_Normedbaseline 6.890e+03
## timefactor2:PandemicFU2 data collected before COVID-19 6.801e+03
## timefactor2:Age_sexFemales 65+ 6.850e+03
## timefactor2:Age_sexMales 45-64 6.847e+03
## timefactor2:Age_sexMales 65+ 6.871e+03
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.156e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.160e+04
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.159e+04
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 6.796e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 6.776e+03
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 6.801e+03
## t value
## (Intercept) 15.363
## timefactor2 3.988
## PandemicFU2 data collected before COVID-19 2.392
## Age_sexFemales 65+ -1.329
## Age_sexMales 45-64 -0.347
## Age_sexMales 65+ -1.049
## EducationHigh School Diploma 2.316
## EducationLess than High School Diploma 1.026
## EducationSome College 1.717
## EthnicityWhite 3.422
## IncomeLevel>$150k -0.572
## IncomeLevel$100-150k -0.170
## IncomeLevel$20-50k -1.750
## IncomeLevel$50-100k -0.078
## BMI -1.619
## CESD.10baseline -1.730
## SmokingStatusFormer Smoker 1.916
## SmokingStatusNever Smoked 1.738
## SmokingStatusOccasional Smoker 0.253
## RelationshipstatusMarried 0.302
## RelationshipstatusSeparated 1.192
## RelationshipstatusSingle 0.706
## RelationshipstatusWidowed -0.662
## LivingstatusAssisted Living -0.380
## LivingstatusHouse 1.783
## LivingstatusOther -0.883
## AnxietyYes 1.015
## MoodDisordYes 0.692
## Chronicconditions -0.792
## PASE_TOTALbaseline 2.401
## Animal_Fluency_Normedbaseline 66.245
## timefactor2:PandemicFU2 data collected before COVID-19 -1.195
## timefactor2:Age_sexFemales 65+ -2.898
## timefactor2:Age_sexMales 45-64 -1.857
## timefactor2:Age_sexMales 65+ -4.109
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.065
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -2.072
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.063
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.869
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.598
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.026
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 6.74e-05
## PandemicFU2 data collected before COVID-19 0.016765
## Age_sexFemales 65+ 0.183882
## Age_sexMales 45-64 0.728414
## Age_sexMales 65+ 0.293989
## EducationHigh School Diploma 0.020603
## EducationLess than High School Diploma 0.305091
## EducationSome College 0.085969
## EthnicityWhite 0.000625
## IncomeLevel>$150k 0.567320
## IncomeLevel$100-150k 0.864833
## IncomeLevel$20-50k 0.080161
## IncomeLevel$50-100k 0.938038
## BMI 0.105527
## CESD.10baseline 0.083648
## SmokingStatusFormer Smoker 0.055380
## SmokingStatusNever Smoked 0.082221
## SmokingStatusOccasional Smoker 0.800339
## RelationshipstatusMarried 0.762643
## RelationshipstatusSeparated 0.233417
## RelationshipstatusSingle 0.480329
## RelationshipstatusWidowed 0.507731
## LivingstatusAssisted Living 0.703973
## LivingstatusHouse 0.074687
## LivingstatusOther 0.377377
## AnxietyYes 0.310043
## MoodDisordYes 0.489208
## Chronicconditions 0.428297
## PASE_TOTALbaseline 0.016396
## Animal_Fluency_Normedbaseline < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19 0.232318
## timefactor2:Age_sexFemales 65+ 0.003762
## timefactor2:Age_sexMales 45-64 0.063331
## timefactor2:Age_sexMales 65+ 4.02e-05
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.947941
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.038316
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.287976
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.384943
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.110189
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.042776
##
## (Intercept) ***
## timefactor2 ***
## PandemicFU2 data collected before COVID-19 *
## Age_sexFemales 65+
## Age_sexMales 45-64
## Age_sexMales 65+
## EducationHigh School Diploma *
## EducationLess than High School Diploma
## EducationSome College .
## EthnicityWhite ***
## IncomeLevel>$150k
## IncomeLevel$100-150k
## IncomeLevel$20-50k .
## IncomeLevel$50-100k
## BMI
## CESD.10baseline .
## SmokingStatusFormer Smoker .
## SmokingStatusNever Smoked .
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed
## LivingstatusAssisted Living
## LivingstatusHouse .
## LivingstatusOther
## AnxietyYes
## MoodDisordYes
## Chronicconditions
## PASE_TOTALbaseline *
## Animal_Fluency_Normedbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19
## timefactor2:Age_sexFemales 65+ **
## timefactor2:Age_sexMales 45-64 .
## timefactor2:Age_sexMales 65+ ***
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 *
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 41 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.887211 0.1766120 Inf 9.541058 10.233365
## FU2 data collected before COVID-19 10.124076 0.1762762 Inf 9.778581 10.469571
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 10.199304 0.1769695 Inf 9.852451 10.546158
## FU2 data collected before COVID-19 10.308415 0.1763161 Inf 9.962842 10.653988
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.712325 0.1913307 Inf 9.337323 10.087326
## FU2 data collected before COVID-19 9.960424 0.1915964 Inf 9.584902 10.335946
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.631510 0.1922524 Inf 9.254702 10.008318
## FU2 data collected before COVID-19 9.913496 0.1919979 Inf 9.537187 10.289805
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.853796 0.1711636 Inf 9.518322 10.189271
## FU2 data collected before COVID-19 9.792729 0.1818446 Inf 9.436320 10.149138
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.976909 0.1713103 Inf 9.641147 10.312671
## FU2 data collected before COVID-19 10.035860 0.1818283 Inf 9.679483 10.392237
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.758995 0.1900380 Inf 9.386527 10.131463
## FU2 data collected before COVID-19 9.815993 0.1908964 Inf 9.441843 10.190143
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 9.538675 0.1911423 Inf 9.164043 9.913307
## FU2 data collected before COVID-19 9.839597 0.1910988 Inf 9.465050 10.214143
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.23686421 0.09901692 Inf -2.392 0.0167
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.10911054 0.09982673 Inf -1.093 0.2744
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.24809925 0.14106932 Inf -1.759 0.0786
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.28198632 0.14292097 Inf -1.973 0.0485
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 0.06106789 0.10461039 Inf 0.584 0.5594
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.05895032 0.10473753 Inf -0.563 0.5735
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.05699842 0.13747589 Inf -0.415 0.6784
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.30092188 0.13918288 Inf -2.162 0.0306
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelAnimals_adj11, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.23686421 0.09901692 Inf -0.4309338 -0.04279462
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.10911054 0.09982673 Inf -0.3047673 0.08654626
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.24809925 0.14106932 Inf -0.5245900 0.02839155
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.28198632 0.14292097 Inf -0.5621063 -0.00186636
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 0.06106789 0.10461039 Inf -0.1439647 0.26610049
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.05895032 0.10473753 Inf -0.2642321 0.14633148
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.05699842 0.13747589 Inf -0.3264462 0.21244937
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.30092188 0.13918288 Inf -0.5737153 -0.02812844
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
Animals_lsmeans_adj11 <- summary(lsmeans(modelAnimals_adj11, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 13639' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 13639)' or larger];
## but be warned that this may result in large computation time and memory use.
Animals_lsmeans_adj11$Time<-NA
Animals_lsmeans_adj11$Time[Animals_lsmeans_adj11$timefactor==1]<-"Follow-up 1"
Animals_lsmeans_adj11$Time[Animals_lsmeans_adj11$timefactor==2]<-"Follow-up 2"
ggplot(Animals_lsmeans_adj11, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
labs(x = "Time", y = "Animal Fluency Normalized Score", title = "Animal Fluency Normalized Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
modelPASE_5<- lmer(PASE_TOTAL~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
(1|ID), data= truncated.data_long)
summary(modelPASE_5)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PASE_TOTAL ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + (1 | ID)
## Data: truncated.data_long
##
## REML criterion at convergence: 117645.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4663 -0.5357 -0.0469 0.4911 6.5485
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1715 41.41
## Residual 2618 51.17
## Number of obs: 10557, groups: ID, 7363
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 354.59172 8.99882
## timefactor2 -3.37673 2.03446
## timefactor3 -22.46835 2.10939
## PandemicFU2 data collected before COVID-19 -4.82709 1.60827
## Age -3.32451 0.08533
## SexM 21.39900 1.58053
## EducationHigh School Diploma -0.05411 2.27081
## EducationLess than High School Diploma 5.24321 3.27150
## EducationSome College 8.73991 2.77199
## EthnicityWhite 3.04163 4.38339
## IncomeLevel>$150k 37.94421 4.04081
## IncomeLevel$100-150k 39.03115 3.30693
## IncomeLevel$20-50k 18.54593 2.25777
## IncomeLevel$50-100k 27.18483 2.40637
## BMI -0.72893 0.15382
## CESD.20.1 -0.51776 0.18125
## SmokingStatusFormer Smoker 1.62104 3.09435
## SmokingStatusNever Smoked 7.96421 3.21572
## SmokingStatusOccasional Smoker 2.36244 6.08465
## RelationshipstatusMarried -9.44065 2.62506
## RelationshipstatusSeparated -1.61135 5.11007
## RelationshipstatusSingle -19.33857 3.57448
## RelationshipstatusWidowed -6.53039 3.65882
## LivingstatusAssisted Living 1.87228 11.30287
## LivingstatusHouse 25.01274 2.38592
## LivingstatusOther 21.37458 8.95714
## AnxietyYes -7.38451 3.14457
## MoodDisordYes -9.07532 2.26153
## Chronicconditions -1.71401 0.36873
## timefactor2:PandemicFU2 data collected before COVID-19 -3.52538 3.12451
## timefactor3:PandemicFU2 data collected before COVID-19 5.42835 2.98792
## df t value
## (Intercept) 7156.61916 39.404
## timefactor2 5779.91261 -1.660
## timefactor3 5780.37350 -10.652
## PandemicFU2 data collected before COVID-19 9646.34237 -3.001
## Age 7013.83415 -38.958
## SexM 6950.60590 13.539
## EducationHigh School Diploma 7197.12755 -0.024
## EducationLess than High School Diploma 7679.22551 1.603
## EducationSome College 6797.73017 3.153
## EthnicityWhite 7052.93348 0.694
## IncomeLevel>$150k 6799.41747 9.390
## IncomeLevel$100-150k 6869.59780 11.803
## IncomeLevel$20-50k 7150.39809 8.214
## IncomeLevel$50-100k 7043.76907 11.297
## BMI 7386.87540 -4.739
## CESD.20.1 7064.35971 -2.857
## SmokingStatusFormer Smoker 7299.56020 0.524
## SmokingStatusNever Smoked 7278.66498 2.477
## SmokingStatusOccasional Smoker 6756.91093 0.388
## RelationshipstatusMarried 6895.83633 -3.596
## RelationshipstatusSeparated 6989.58912 -0.315
## RelationshipstatusSingle 6923.77551 -5.410
## RelationshipstatusWidowed 7044.98777 -1.785
## LivingstatusAssisted Living 6756.19138 0.166
## LivingstatusHouse 6875.22038 10.483
## LivingstatusOther 7411.75562 2.386
## AnxietyYes 7047.21420 -2.348
## MoodDisordYes 6936.59744 -4.013
## Chronicconditions 6897.43328 -4.648
## timefactor2:PandemicFU2 data collected before COVID-19 5729.99822 -1.128
## timefactor3:PandemicFU2 data collected before COVID-19 5685.85129 1.817
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 0.097015 .
## timefactor3 < 2e-16 ***
## PandemicFU2 data collected before COVID-19 0.002694 **
## Age < 2e-16 ***
## SexM < 2e-16 ***
## EducationHigh School Diploma 0.980990
## EducationLess than High School Diploma 0.109043
## EducationSome College 0.001623 **
## EthnicityWhite 0.487769
## IncomeLevel>$150k < 2e-16 ***
## IncomeLevel$100-150k < 2e-16 ***
## IncomeLevel$20-50k 2.51e-16 ***
## IncomeLevel$50-100k < 2e-16 ***
## BMI 2.19e-06 ***
## CESD.20.1 0.004296 **
## SmokingStatusFormer Smoker 0.600384
## SmokingStatusNever Smoked 0.013285 *
## SmokingStatusOccasional Smoker 0.697834
## RelationshipstatusMarried 0.000325 ***
## RelationshipstatusSeparated 0.752522
## RelationshipstatusSingle 6.51e-08 ***
## RelationshipstatusWidowed 0.074331 .
## LivingstatusAssisted Living 0.868440
## LivingstatusHouse < 2e-16 ***
## LivingstatusOther 0.017043 *
## AnxietyYes 0.018885 *
## MoodDisordYes 6.06e-05 ***
## Chronicconditions 3.41e-06 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.259241
## timefactor3:PandemicFU2 data collected before COVID-19 0.069305 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelPASE_5, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 166.1802 4.882968 Inf 156.6098 175.7507
## FU2 data collected before COVID-19 161.3531 4.946697 Inf 151.6578 171.0485
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 162.8035 5.165208 Inf 152.6799 172.9271
## FU2 data collected before COVID-19 154.4510 5.318386 Inf 144.0272 164.8749
##
## timefactor = 3:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 143.7119 5.187923 Inf 133.5437 153.8800
## FU2 data collected before COVID-19 144.3132 5.214719 Inf 134.0925 154.5338
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelPASE_5, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 4.827089 1.608266 Inf 3.001 0.0027
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 8.352467 3.024190 Inf 2.762 0.0057
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.601265 2.882554 Inf -0.209 0.8348
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelPASE_5, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 4.827089 1.608266 Inf 1.674945 7.979233
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 8.352467 3.024190 Inf 2.425164 14.279771
##
## timefactor = 3:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.601265 2.882554 Inf -6.250967 5.048437
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
PASE_lsmeans_5 <- summary(lsmeans(modelPASE_5, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
PASE_lsmeans_5$Time<-NA
PASE_lsmeans_5$Time[PASE_lsmeans_5$timefactor==1]<-"Baseline"
PASE_lsmeans_5$Time[PASE_lsmeans_5$timefactor==2]<-"Follow-up 1"
PASE_lsmeans_5$Time[PASE_lsmeans_5$timefactor==3]<-"Follow-up 2"
ggplot(PASE_lsmeans_5, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from Baseline to FU2 by Pandemic status") +
theme_bw()
modelPASE_6<- lmer(PASE_TOTAL ~ timefactor*Pandemic + Age + Sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= truncated.data_long_2)
summary(modelPASE_6)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: PASE_TOTAL ~ timefactor * Pandemic + Age + Sex + Education +
## Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
## Relationshipstatus + Livingstatus + Anxiety + MoodDisord +
## Chronicconditions + PASE_TOTALbaseline + (1 | ID)
## Data: truncated.data_long_2
##
## REML criterion at convergence: 34098.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6805 -0.5406 -0.0438 0.5107 4.5754
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1216 34.87
## Residual 2223 47.15
## Number of obs: 3125, groups: ID, 2543
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 232.73128 15.86106
## timefactor2 -19.62725 2.64845
## PandemicFU2 data collected before COVID-19 -5.30365 3.06354
## Age -2.36897 0.14780
## SexM 18.17102 2.52075
## EducationHigh School Diploma -11.08048 3.74359
## EducationLess than High School Diploma -8.20565 6.08434
## EducationSome College 5.41348 4.24920
## EthnicityWhite 11.18200 7.07555
## IncomeLevel>$150k 11.54903 6.28360
## IncomeLevel$100-150k 13.26436 5.18297
## IncomeLevel$20-50k 6.06846 3.73566
## IncomeLevel$50-100k 9.01259 3.90698
## BMI -0.50866 0.26619
## CESD.10baseline 0.29557 0.29246
## SmokingStatusFormer Smoker 4.30350 5.28360
## SmokingStatusNever Smoked 7.29594 5.46236
## SmokingStatusOccasional Smoker 0.86889 9.28644
## RelationshipstatusMarried -3.04107 4.06275
## RelationshipstatusSeparated 9.25215 8.04699
## RelationshipstatusSingle -6.55354 5.61456
## RelationshipstatusWidowed -0.69439 5.87389
## LivingstatusAssisted Living -13.41829 17.10814
## LivingstatusHouse 13.36191 3.74568
## LivingstatusOther 37.92922 15.38442
## AnxietyYes -3.58410 5.05293
## MoodDisordYes -11.36014 3.55337
## Chronicconditions -1.63359 0.57824
## PASE_TOTALbaseline 0.36106 0.01686
## timefactor2:PandemicFU2 data collected before COVID-19 9.13538 3.86024
## df t value
## (Intercept) 2433.40877 14.673
## timefactor2 1845.93403 -7.411
## PandemicFU2 data collected before COVID-19 3079.70469 -1.731
## Age 2437.60274 -16.028
## SexM 2423.85638 7.209
## EducationHigh School Diploma 2429.98070 -2.960
## EducationLess than High School Diploma 2440.54924 -1.349
## EducationSome College 2338.14352 1.274
## EthnicityWhite 2585.73345 1.580
## IncomeLevel>$150k 2381.36205 1.838
## IncomeLevel$100-150k 2437.66731 2.559
## IncomeLevel$20-50k 2373.86541 1.624
## IncomeLevel$50-100k 2376.39493 2.307
## BMI 2435.29043 -1.911
## CESD.10baseline 2398.83981 1.011
## SmokingStatusFormer Smoker 2359.64679 0.815
## SmokingStatusNever Smoked 2358.57043 1.336
## SmokingStatusOccasional Smoker 2330.59744 0.094
## RelationshipstatusMarried 2463.12175 -0.749
## RelationshipstatusSeparated 2362.19214 1.150
## RelationshipstatusSingle 2355.45363 -1.167
## RelationshipstatusWidowed 2401.80448 -0.118
## LivingstatusAssisted Living 2279.55813 -0.784
## LivingstatusHouse 2423.26416 3.567
## LivingstatusOther 2414.59723 2.465
## AnxietyYes 2382.38859 -0.709
## MoodDisordYes 2437.12530 -3.197
## Chronicconditions 2361.78407 -2.825
## PASE_TOTALbaseline 2398.87470 21.419
## timefactor2:PandemicFU2 data collected before COVID-19 1763.51326 2.367
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## timefactor2 1.90e-13 ***
## PandemicFU2 data collected before COVID-19 0.083513 .
## Age < 2e-16 ***
## SexM 7.52e-13 ***
## EducationHigh School Diploma 0.003108 **
## EducationLess than High School Diploma 0.177574
## EducationSome College 0.202790
## EthnicityWhite 0.114144
## IncomeLevel>$150k 0.066192 .
## IncomeLevel$100-150k 0.010551 *
## IncomeLevel$20-50k 0.104409
## IncomeLevel$50-100k 0.021152 *
## BMI 0.056135 .
## CESD.10baseline 0.312300
## SmokingStatusFormer Smoker 0.415439
## SmokingStatusNever Smoked 0.181784
## SmokingStatusOccasional Smoker 0.925462
## RelationshipstatusMarried 0.454215
## RelationshipstatusSeparated 0.250357
## RelationshipstatusSingle 0.243231
## RelationshipstatusWidowed 0.905906
## LivingstatusAssisted Living 0.432933
## LivingstatusHouse 0.000368 ***
## LivingstatusOther 0.013754 *
## AnxietyYes 0.478201
## MoodDisordYes 0.001406 **
## Chronicconditions 0.004766 **
## PASE_TOTALbaseline < 2e-16 ***
## timefactor2:PandemicFU2 data collected before COVID-19 0.018063 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 30 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelPASE_6, ~Pandemic|timefactor)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 162.9041 8.109685 Inf 147.0094 178.7988
## FU2 data collected before COVID-19 157.6005 8.156860 Inf 141.6133 173.5876
##
## timefactor = 2:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 143.2769 8.068424 Inf 127.4631 159.0907
## FU2 data collected before COVID-19 147.1086 8.127680 Inf 131.1787 163.0386
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelPASE_6, ~Pandemic|timefactor), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 5.303654 3.063540 Inf 1.731 0.0834
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -3.831727 2.919848 Inf -1.312 0.1894
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelPASE_6, ~Pandemic|timefactor), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 5.303654 3.063540 Inf -0.700774 11.308082
##
## timefactor = 2:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -3.831727 2.919848 Inf -9.554524 1.891069
##
## Results are averaged over the levels of: Sex, Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
PASE_lsmeans_6 <- summary(lsmeans(modelPASE_6, ~timefactor|Pandemic))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
PASE_lsmeans_6$Time<-NA
PASE_lsmeans_6$Time[PASE_lsmeans_6$timefactor==1]<-"Follow-up 1"
PASE_lsmeans_6$Time[PASE_lsmeans_6$timefactor==2]<-"Follow-up 2"
ggplot(PASE_lsmeans_6, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
modelPASE_7<- lmer(PASE_TOTAL ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.20.1 + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
(1|ID), data= truncated.data_long)
summary(modelPASE_7)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PASE_TOTAL ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +
## IncomeLevel + BMI + CESD.20.1 + SmokingStatus + Relationshipstatus +
## Livingstatus + Anxiety + MoodDisord + Chronicconditions + (1 | ID)
## Data: truncated.data_long
##
## REML criterion at convergence: 118108.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4851 -0.5420 -0.0355 0.4895 6.0021
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 2055 45.33
## Residual 2589 50.88
## Number of obs: 10557, groups: ID, 7363
##
## Fixed effects:
## Estimate
## (Intercept) 170.4204
## timefactor2 -0.6545
## timefactor3 -26.4561
## PandemicFU2 data collected before COVID-19 -7.6101
## Age_sexFemales 65+ -45.9418
## Age_sexMales 45-64 24.2064
## Age_sexMales 65+ -41.9239
## EducationHigh School Diploma -1.1925
## EducationLess than High School Diploma 1.1666
## EducationSome College 5.9158
## EthnicityWhite -2.2806
## IncomeLevel>$150k 42.9040
## IncomeLevel$100-150k 44.7535
## IncomeLevel$20-50k 19.4238
## IncomeLevel$50-100k 30.8084
## BMI -0.5189
## CESD.20.1 -0.3040
## SmokingStatusFormer Smoker -2.4601
## SmokingStatusNever Smoked 5.0911
## SmokingStatusOccasional Smoker 1.4476
## RelationshipstatusMarried -7.5952
## RelationshipstatusSeparated 2.8706
## RelationshipstatusSingle -15.9594
## RelationshipstatusWidowed -19.7036
## LivingstatusAssisted Living -11.2470
## LivingstatusHouse 28.7250
## LivingstatusOther 26.3489
## AnxietyYes -3.9435
## MoodDisordYes -8.1663
## Chronicconditions -3.1997
## timefactor2:PandemicFU2 data collected before COVID-19 -7.4625
## timefactor3:PandemicFU2 data collected before COVID-19 7.8750
## timefactor2:Age_sexFemales 65+ -10.0769
## timefactor3:Age_sexFemales 65+ 0.4537
## timefactor2:Age_sexMales 45-64 -5.8609
## timefactor3:Age_sexMales 45-64 9.0115
## timefactor2:Age_sexMales 65+ 4.7942
## timefactor3:Age_sexMales 65+ -1.1259
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 5.0377
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -6.5782
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 10.1817
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 11.6768
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 2.5661
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 12.3580
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -8.9748
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -10.1173
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 5.0424
## Std. Error
## (Intercept) 7.8324
## timefactor2 3.6405
## timefactor3 3.7155
## PandemicFU2 data collected before COVID-19 2.8302
## Age_sexFemales 65+ 3.7711
## Age_sexMales 45-64 2.7433
## Age_sexMales 65+ 3.4862
## EducationHigh School Diploma 2.3696
## EducationLess than High School Diploma 3.4097
## EducationSome College 2.8947
## EthnicityWhite 4.5716
## IncomeLevel>$150k 4.2171
## IncomeLevel$100-150k 3.4454
## IncomeLevel$20-50k 2.3586
## IncomeLevel$50-100k 2.5116
## BMI 0.1602
## CESD.20.1 0.1890
## SmokingStatusFormer Smoker 3.2238
## SmokingStatusNever Smoked 3.3555
## SmokingStatusOccasional Smoker 6.3564
## RelationshipstatusMarried 2.7474
## RelationshipstatusSeparated 5.3341
## RelationshipstatusSingle 3.7334
## RelationshipstatusWidowed 3.8063
## LivingstatusAssisted Living 11.8066
## LivingstatusHouse 2.4908
## LivingstatusOther 9.3461
## AnxietyYes 3.2799
## MoodDisordYes 2.3636
## Chronicconditions 0.3811
## timefactor2:PandemicFU2 data collected before COVID-19 5.2293
## timefactor3:PandemicFU2 data collected before COVID-19 4.9455
## timefactor2:Age_sexFemales 65+ 6.7449
## timefactor3:Age_sexFemales 65+ 6.9969
## timefactor2:Age_sexMales 45-64 4.8875
## timefactor3:Age_sexMales 45-64 4.9782
## timefactor2:Age_sexMales 65+ 6.2795
## timefactor3:Age_sexMales 65+ 6.8235
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 4.9332
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 4.1119
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 4.8518
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 9.4198
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 9.1762
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 7.8248
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 7.4449
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 9.4964
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 9.2345
## df
## (Intercept) 7461.1624
## timefactor2 5421.5375
## timefactor3 5386.2216
## PandemicFU2 data collected before COVID-19 9593.6178
## Age_sexFemales 65+ 9374.7818
## Age_sexMales 45-64 9527.0386
## Age_sexMales 65+ 9445.5375
## EducationHigh School Diploma 7299.8641
## EducationLess than High School Diploma 7711.0961
## EducationSome College 6939.3583
## EthnicityWhite 7159.0380
## IncomeLevel>$150k 6938.2991
## IncomeLevel$100-150k 6998.5271
## IncomeLevel$20-50k 7251.3329
## IncomeLevel$50-100k 7162.7393
## BMI 7472.1519
## CESD.20.1 7181.1546
## SmokingStatusFormer Smoker 7389.4401
## SmokingStatusNever Smoked 7369.3219
## SmokingStatusOccasional Smoker 6902.4387
## RelationshipstatusMarried 7043.8296
## RelationshipstatusSeparated 7111.4771
## RelationshipstatusSingle 7064.2457
## RelationshipstatusWidowed 7156.1943
## LivingstatusAssisted Living 6905.9426
## LivingstatusHouse 7007.5093
## LivingstatusOther 7497.6452
## AnxietyYes 7163.4535
## MoodDisordYes 7061.4096
## Chronicconditions 7053.3850
## timefactor2:PandemicFU2 data collected before COVID-19 5429.0438
## timefactor3:PandemicFU2 data collected before COVID-19 5297.6457
## timefactor2:Age_sexFemales 65+ 5620.7590
## timefactor3:Age_sexFemales 65+ 5728.6722
## timefactor2:Age_sexMales 45-64 5531.8559
## timefactor3:Age_sexMales 45-64 5511.4953
## timefactor2:Age_sexMales 65+ 5431.3158
## timefactor3:Age_sexMales 65+ 5447.5955
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 9595.7201
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 9583.3879
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 9536.6153
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 5542.7462
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 5613.7398
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 5473.7923
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 5413.5081
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 5455.5728
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 5433.4943
## t value
## (Intercept) 21.758
## timefactor2 -0.180
## timefactor3 -7.121
## PandemicFU2 data collected before COVID-19 -2.689
## Age_sexFemales 65+ -12.183
## Age_sexMales 45-64 8.824
## Age_sexMales 65+ -12.026
## EducationHigh School Diploma -0.503
## EducationLess than High School Diploma 0.342
## EducationSome College 2.044
## EthnicityWhite -0.499
## IncomeLevel>$150k 10.174
## IncomeLevel$100-150k 12.989
## IncomeLevel$20-50k 8.235
## IncomeLevel$50-100k 12.266
## BMI -3.239
## CESD.20.1 -1.608
## SmokingStatusFormer Smoker -0.763
## SmokingStatusNever Smoked 1.517
## SmokingStatusOccasional Smoker 0.228
## RelationshipstatusMarried -2.765
## RelationshipstatusSeparated 0.538
## RelationshipstatusSingle -4.275
## RelationshipstatusWidowed -5.177
## LivingstatusAssisted Living -0.953
## LivingstatusHouse 11.532
## LivingstatusOther 2.819
## AnxietyYes -1.202
## MoodDisordYes -3.455
## Chronicconditions -8.397
## timefactor2:PandemicFU2 data collected before COVID-19 -1.427
## timefactor3:PandemicFU2 data collected before COVID-19 1.592
## timefactor2:Age_sexFemales 65+ -1.494
## timefactor3:Age_sexFemales 65+ 0.065
## timefactor2:Age_sexMales 45-64 -1.199
## timefactor3:Age_sexMales 45-64 1.810
## timefactor2:Age_sexMales 65+ 0.763
## timefactor3:Age_sexMales 65+ -0.165
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.021
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.600
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 2.099
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.240
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.280
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1.579
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.206
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -1.065
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.546
## Pr(>|t|)
## (Intercept) < 2e-16
## timefactor2 0.857334
## timefactor3 1.22e-12
## PandemicFU2 data collected before COVID-19 0.007182
## Age_sexFemales 65+ < 2e-16
## Age_sexMales 45-64 < 2e-16
## Age_sexMales 65+ < 2e-16
## EducationHigh School Diploma 0.614802
## EducationLess than High School Diploma 0.732250
## EducationSome College 0.041026
## EthnicityWhite 0.617896
## IncomeLevel>$150k < 2e-16
## IncomeLevel$100-150k < 2e-16
## IncomeLevel$20-50k < 2e-16
## IncomeLevel$50-100k < 2e-16
## BMI 0.001203
## CESD.20.1 0.107863
## SmokingStatusFormer Smoker 0.445423
## SmokingStatusNever Smoked 0.129240
## SmokingStatusOccasional Smoker 0.819854
## RelationshipstatusMarried 0.005715
## RelationshipstatusSeparated 0.590476
## RelationshipstatusSingle 1.94e-05
## RelationshipstatusWidowed 2.32e-07
## LivingstatusAssisted Living 0.340824
## LivingstatusHouse < 2e-16
## LivingstatusOther 0.004826
## AnxietyYes 0.229282
## MoodDisordYes 0.000554
## Chronicconditions < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19 0.153625
## timefactor3:PandemicFU2 data collected before COVID-19 0.111366
## timefactor2:Age_sexFemales 65+ 0.135232
## timefactor3:Age_sexFemales 65+ 0.948300
## timefactor2:Age_sexMales 45-64 0.230515
## timefactor3:Age_sexMales 45-64 0.070322
## timefactor2:Age_sexMales 65+ 0.445211
## timefactor3:Age_sexMales 65+ 0.868944
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.307192
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.109675
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.035884
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.215176
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.779761
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.114317
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.228063
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.286746
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.585061
##
## (Intercept) ***
## timefactor2
## timefactor3 ***
## PandemicFU2 data collected before COVID-19 **
## Age_sexFemales 65+ ***
## Age_sexMales 45-64 ***
## Age_sexMales 65+ ***
## EducationHigh School Diploma
## EducationLess than High School Diploma
## EducationSome College *
## EthnicityWhite
## IncomeLevel>$150k ***
## IncomeLevel$100-150k ***
## IncomeLevel$20-50k ***
## IncomeLevel$50-100k ***
## BMI **
## CESD.20.1
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried **
## RelationshipstatusSeparated
## RelationshipstatusSingle ***
## RelationshipstatusWidowed ***
## LivingstatusAssisted Living
## LivingstatusHouse ***
## LivingstatusOther **
## AnxietyYes
## MoodDisordYes ***
## Chronicconditions ***
## timefactor2:PandemicFU2 data collected before COVID-19
## timefactor3:PandemicFU2 data collected before COVID-19
## timefactor2:Age_sexFemales 65+
## timefactor3:Age_sexFemales 65+
## timefactor2:Age_sexMales 45-64
## timefactor3:Age_sexMales 45-64 .
## timefactor2:Age_sexMales 65+
## timefactor3:Age_sexMales 65+
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ *
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor3:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 47 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelPASE_7, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 172.3904 5.418116 Inf 161.77110 183.0097
## FU2 data collected before COVID-19 164.7803 5.419626 Inf 154.15802 175.4026
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 171.7359 6.134535 Inf 159.71247 183.7594
## FU2 data collected before COVID-19 156.6633 6.225348 Inf 144.46186 168.8648
##
## timefactor = 3, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 145.9343 6.195971 Inf 133.79040 158.0782
## FU2 data collected before COVID-19 146.1992 5.964233 Inf 134.50947 157.8888
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 126.4487 5.797846 Inf 115.08508 137.8122
## FU2 data collected before COVID-19 123.8763 5.818754 Inf 112.47172 135.2808
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 115.7173 7.492599 Inf 101.03204 130.4025
## FU2 data collected before COVID-19 117.3592 7.314920 Inf 103.02221 131.6962
##
## timefactor = 3, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 100.4462 7.585409 Inf 85.57911 115.3134
## FU2 data collected before COVID-19 108.3149 6.998143 Inf 94.59880 122.0310
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 196.5968 5.267884 Inf 186.27193 206.9217
## FU2 data collected before COVID-19 182.4085 5.567387 Inf 171.49661 193.3204
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 190.0815 5.907899 Inf 178.50219 201.6607
## FU2 data collected before COVID-19 180.7886 6.865830 Inf 167.33182 194.2454
##
## timefactor = 3, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 179.1521 5.942304 Inf 167.50544 190.7988
## FU2 data collected before COVID-19 163.8640 6.636062 Inf 150.85759 176.8705
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 130.4665 5.782253 Inf 119.13346 141.7995
## FU2 data collected before COVID-19 133.0380 5.798126 Inf 121.67393 144.4022
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 134.6062 7.134851 Inf 120.62218 148.5903
## FU2 data collected before COVID-19 119.5980 7.803670 Inf 104.30308 134.8929
##
## timefactor = 3, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 102.8844 7.596903 Inf 87.99476 117.7741
## FU2 data collected before COVID-19 118.3734 7.173194 Inf 104.31419 132.4326
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelPASE_7, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 7.610119 2.830240 Inf 2.689 0.0072
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 15.072620 5.070876 Inf 2.972 0.0030
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -0.264873 4.789260 Inf -0.055 0.9559
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 2.572386 4.051453 Inf 0.635 0.5255
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -1.641929 7.622946 Inf -0.215 0.8295
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -7.868669 7.492699 Inf -1.050 0.2936
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 14.188308 2.989910 Inf 4.745 <.0001
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 9.292856 5.672901 Inf 1.638 0.1014
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 15.288108 5.407340 Inf 2.827 0.0047
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -2.571581 3.947393 Inf -0.651 0.5147
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 15.008245 7.756102 Inf 1.935 0.0530
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -15.488976 7.617879 Inf -2.033 0.0420
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelPASE_7, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 7.610119 2.830240 Inf 2.062950 13.157288
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 15.072620 5.070876 Inf 5.133886 25.011354
##
## timefactor = 3, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -0.264873 4.789260 Inf -9.651650 9.121904
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 2.572386 4.051453 Inf -5.368316 10.513087
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -1.641929 7.622946 Inf -16.582628 13.298771
##
## timefactor = 3, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -7.868669 7.492699 Inf -22.554089 6.816750
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 14.188308 2.989910 Inf 8.328191 20.048424
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 9.292856 5.672901 Inf -1.825824 20.411537
##
## timefactor = 3, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 15.288108 5.407340 Inf 4.689915 25.886300
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -2.571581 3.947393 Inf -10.308330 5.165168
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 15.008245 7.756102 Inf -0.193437 30.209926
##
## timefactor = 3, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -15.488976 7.617879 Inf -30.419745 -0.558206
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
PASE_lsmeans_7 <- summary(lsmeans(modelPASE_7, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 10557' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 10557)' or larger];
## but be warned that this may result in large computation time and memory use.
PASE_lsmeans_7$Time<-NA
PASE_lsmeans_7$Time[PASE_lsmeans_7$timefactor==1]<-"Baseline"
PASE_lsmeans_7$Time[PASE_lsmeans_7$timefactor==2]<-"Follow-up 1"
PASE_lsmeans_7$Time[PASE_lsmeans_7$timefactor==3]<-"Follow-up 2"
ggplot(PASE_lsmeans_7, aes(x = Time, y = lsmean, group=Pandemic)) +
geom_line(aes(linetype = Pandemic)) + facet_wrap(~Age_sex, scales = "free") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), width = 0.2) +
geom_point(aes(y = lsmean), size = 3, shape = 21, fill = "white") +
labs(x = "Time", y = "MAT Score", title = "PASE Total Score from Baseline to FU2 by Pandemic status") +
theme_bw()
modelPASE_8<- lmer(PASE_TOTAL ~ timefactor*Pandemic*Age_sex + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions + PASE_TOTALbaseline +
(1|ID), data= truncated.data_long_2)
summary(modelPASE_8)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## PASE_TOTAL ~ timefactor * Pandemic * Age_sex + Education + Ethnicity +
## IncomeLevel + BMI + CESD.10baseline + SmokingStatus + Relationshipstatus +
## Livingstatus + Anxiety + MoodDisord + Chronicconditions +
## PASE_TOTALbaseline + (1 | ID)
## Data: truncated.data_long_2
##
## REML criterion at convergence: 34149.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5698 -0.5427 -0.0392 0.4979 4.0877
##
## Random effects:
## Groups Name Variance Std.Dev.
## ID (Intercept) 1398 37.39
## Residual 2202 46.92
## Number of obs: 3125, groups: ID, 2543
##
## Fixed effects:
## Estimate
## (Intercept) 95.4964
## timefactor2 -25.7490
## PandemicFU2 data collected before COVID-19 -8.8218
## Age_sexFemales 65+ -35.2604
## Age_sexMales 45-64 13.2060
## Age_sexMales 65+ -18.0100
## EducationHigh School Diploma -11.7484
## EducationLess than High School Diploma -12.0805
## EducationSome College 3.2351
## EthnicityWhite 7.2713
## IncomeLevel>$150k 12.7741
## IncomeLevel$100-150k 14.9002
## IncomeLevel$20-50k 4.6884
## IncomeLevel$50-100k 9.2970
## BMI -0.4072
## CESD.10baseline 0.4619
## SmokingStatusFormer Smoker 2.3891
## SmokingStatusNever Smoked 6.3021
## SmokingStatusOccasional Smoker 3.0490
## RelationshipstatusMarried -1.8200
## RelationshipstatusSeparated 11.7951
## RelationshipstatusSingle -3.8715
## RelationshipstatusWidowed -9.8022
## LivingstatusAssisted Living -21.8178
## LivingstatusHouse 15.5912
## LivingstatusOther 41.5047
## AnxietyYes -1.3635
## MoodDisordYes -11.0153
## Chronicconditions -2.5631
## PASE_TOTALbaseline 0.4154
## timefactor2:PandemicFU2 data collected before COVID-19 13.3645
## timefactor2:Age_sexFemales 65+ 12.8453
## timefactor2:Age_sexMales 45-64 12.0538
## timefactor2:Age_sexMales 65+ -5.2585
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 11.3888
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 4.0639
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -7.7593
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -8.8063
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -14.4876
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 15.9599
## Std. Error
## (Intercept) 13.2006
## timefactor2 4.6306
## PandemicFU2 data collected before COVID-19 5.1185
## Age_sexFemales 65+ 6.9763
## Age_sexMales 45-64 4.8934
## Age_sexMales 65+ 6.2591
## EducationHigh School Diploma 3.8518
## EducationLess than High School Diploma 6.2412
## EducationSome College 4.3676
## EthnicityWhite 7.2563
## IncomeLevel>$150k 6.4611
## IncomeLevel$100-150k 5.3259
## IncomeLevel$20-50k 3.8403
## IncomeLevel$50-100k 4.0208
## BMI 0.2734
## CESD.10baseline 0.3006
## SmokingStatusFormer Smoker 5.4325
## SmokingStatusNever Smoked 5.6200
## SmokingStatusOccasional Smoker 9.5521
## RelationshipstatusMarried 4.2028
## RelationshipstatusSeparated 8.2761
## RelationshipstatusSingle 5.7846
## RelationshipstatusWidowed 6.0223
## LivingstatusAssisted Living 17.6015
## LivingstatusHouse 3.8523
## LivingstatusOther 15.8426
## AnxietyYes 5.1909
## MoodDisordYes 3.6572
## Chronicconditions 0.5931
## PASE_TOTALbaseline 0.0166
## timefactor2:PandemicFU2 data collected before COVID-19 6.3516
## timefactor2:Age_sexFemales 65+ 8.8644
## timefactor2:Age_sexMales 45-64 6.2820
## timefactor2:Age_sexMales 65+ 8.3477
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 9.2772
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 7.6516
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 9.2440
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 11.8532
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 9.6114
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 11.8721
## df
## (Intercept) 2550.9670
## timefactor2 1661.3322
## PandemicFU2 data collected before COVID-19 3047.3970
## Age_sexFemales 65+ 3084.8545
## Age_sexMales 45-64 3083.8475
## Age_sexMales 65+ 3084.8802
## EducationHigh School Diploma 2448.8516
## EducationLess than High School Diploma 2454.2637
## EducationSome College 2361.3771
## EthnicityWhite 2585.0053
## IncomeLevel>$150k 2402.6331
## IncomeLevel$100-150k 2452.4249
## IncomeLevel$20-50k 2389.6708
## IncomeLevel$50-100k 2395.1996
## BMI 2452.6833
## CESD.10baseline 2415.6237
## SmokingStatusFormer Smoker 2380.8832
## SmokingStatusNever Smoked 2378.9700
## SmokingStatusOccasional Smoker 2356.0664
## RelationshipstatusMarried 2479.2893
## RelationshipstatusSeparated 2382.8738
## RelationshipstatusSingle 2380.2978
## RelationshipstatusWidowed 2420.7702
## LivingstatusAssisted Living 2311.1037
## LivingstatusHouse 2441.9050
## LivingstatusOther 2437.9568
## AnxietyYes 2402.2627
## MoodDisordYes 2454.0552
## Chronicconditions 2384.3109
## PASE_TOTALbaseline 2420.2438
## timefactor2:PandemicFU2 data collected before COVID-19 1587.7940
## timefactor2:Age_sexFemales 65+ 1727.9138
## timefactor2:Age_sexMales 45-64 1769.4141
## timefactor2:Age_sexMales 65+ 1847.3384
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 3066.5688
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 3057.8793
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 3065.8730
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1685.9006
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 1693.7893
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1826.7015
## t value
## (Intercept) 7.234
## timefactor2 -5.561
## PandemicFU2 data collected before COVID-19 -1.724
## Age_sexFemales 65+ -5.054
## Age_sexMales 45-64 2.699
## Age_sexMales 65+ -2.877
## EducationHigh School Diploma -3.050
## EducationLess than High School Diploma -1.936
## EducationSome College 0.741
## EthnicityWhite 1.002
## IncomeLevel>$150k 1.977
## IncomeLevel$100-150k 2.798
## IncomeLevel$20-50k 1.221
## IncomeLevel$50-100k 2.312
## BMI -1.489
## CESD.10baseline 1.536
## SmokingStatusFormer Smoker 0.440
## SmokingStatusNever Smoked 1.121
## SmokingStatusOccasional Smoker 0.319
## RelationshipstatusMarried -0.433
## RelationshipstatusSeparated 1.425
## RelationshipstatusSingle -0.669
## RelationshipstatusWidowed -1.628
## LivingstatusAssisted Living -1.240
## LivingstatusHouse 4.047
## LivingstatusOther 2.620
## AnxietyYes -0.263
## MoodDisordYes -3.012
## Chronicconditions -4.322
## PASE_TOTALbaseline 25.029
## timefactor2:PandemicFU2 data collected before COVID-19 2.104
## timefactor2:Age_sexFemales 65+ 1.449
## timefactor2:Age_sexMales 45-64 1.919
## timefactor2:Age_sexMales 65+ -0.630
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 1.228
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.531
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ -0.839
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ -0.743
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 -1.507
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 1.344
## Pr(>|t|)
## (Intercept) 6.16e-13
## timefactor2 3.13e-08
## PandemicFU2 data collected before COVID-19 0.08490
## Age_sexFemales 65+ 4.57e-07
## Age_sexMales 45-64 0.00700
## Age_sexMales 65+ 0.00404
## EducationHigh School Diploma 0.00231
## EducationLess than High School Diploma 0.05303
## EducationSome College 0.45895
## EthnicityWhite 0.31640
## IncomeLevel>$150k 0.04815
## IncomeLevel$100-150k 0.00519
## IncomeLevel$20-50k 0.22227
## IncomeLevel$50-100k 0.02085
## BMI 0.13659
## CESD.10baseline 0.12456
## SmokingStatusFormer Smoker 0.66013
## SmokingStatusNever Smoked 0.26224
## SmokingStatusOccasional Smoker 0.74961
## RelationshipstatusMarried 0.66503
## RelationshipstatusSeparated 0.15423
## RelationshipstatusSingle 0.50339
## RelationshipstatusWidowed 0.10373
## LivingstatusAssisted Living 0.21527
## LivingstatusHouse 5.34e-05
## LivingstatusOther 0.00885
## AnxietyYes 0.79283
## MoodDisordYes 0.00262
## Chronicconditions 1.61e-05
## PASE_TOTALbaseline < 2e-16
## timefactor2:PandemicFU2 data collected before COVID-19 0.03553
## timefactor2:Age_sexFemales 65+ 0.14749
## timefactor2:Age_sexMales 45-64 0.05517
## timefactor2:Age_sexMales 65+ 0.52882
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.21969
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.59538
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.40132
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+ 0.45761
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64 0.13191
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+ 0.17901
##
## (Intercept) ***
## timefactor2 ***
## PandemicFU2 data collected before COVID-19 .
## Age_sexFemales 65+ ***
## Age_sexMales 45-64 **
## Age_sexMales 65+ **
## EducationHigh School Diploma **
## EducationLess than High School Diploma .
## EducationSome College
## EthnicityWhite
## IncomeLevel>$150k *
## IncomeLevel$100-150k **
## IncomeLevel$20-50k
## IncomeLevel$50-100k *
## BMI
## CESD.10baseline
## SmokingStatusFormer Smoker
## SmokingStatusNever Smoked
## SmokingStatusOccasional Smoker
## RelationshipstatusMarried
## RelationshipstatusSeparated
## RelationshipstatusSingle
## RelationshipstatusWidowed
## LivingstatusAssisted Living
## LivingstatusHouse ***
## LivingstatusOther **
## AnxietyYes
## MoodDisordYes **
## Chronicconditions ***
## PASE_TOTALbaseline ***
## timefactor2:PandemicFU2 data collected before COVID-19 *
## timefactor2:Age_sexFemales 65+
## timefactor2:Age_sexMales 45-64 .
## timefactor2:Age_sexMales 65+
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 40 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
lsmeans(modelPASE_8, ~Pandemic|timefactor|Age_sex)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 167.0874 8.864445 Inf 149.71343 184.4614
## FU2 data collected before COVID-19 158.2656 8.868016 Inf 140.88466 175.6466
##
## timefactor = 2, Age_sex = Females 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 141.3385 8.885093 Inf 123.92400 158.7529
## FU2 data collected before COVID-19 145.8812 8.708717 Inf 128.81247 162.9500
##
## timefactor = 1, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 131.8270 9.945986 Inf 112.33321 151.3208
## FU2 data collected before COVID-19 134.3940 9.756196 Inf 115.27226 153.5159
##
## timefactor = 2, Age_sex = Females 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 118.9233 9.906700 Inf 99.50656 138.3401
## FU2 data collected before COVID-19 126.0486 9.563616 Inf 107.30428 144.7930
##
## timefactor = 1, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 180.2934 8.737358 Inf 163.16850 197.4183
## FU2 data collected before COVID-19 175.5355 9.323519 Inf 157.26175 193.8093
##
## timefactor = 2, Age_sex = Males 45-64:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 166.5983 8.708821 Inf 149.52927 183.6672
## FU2 data collected before COVID-19 160.7173 9.214853 Inf 142.65654 178.7781
##
## timefactor = 1, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 149.0774 9.650794 Inf 130.16217 167.9926
## FU2 data collected before COVID-19 132.4963 10.063831 Inf 112.77153 152.2210
##
## timefactor = 2, Age_sex = Males 65+:
## Pandemic lsmean SE df asymp.LCL asymp.UCL
## FU2 data collected after COVID-19 118.0700 9.998764 Inf 98.47274 137.6672
## FU2 data collected before COVID-19 130.8133 9.606062 Inf 111.98578 149.6409
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
contrast(lsmeans(modelPASE_8, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none")
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 8.821770 5.118474 Inf 1.724 0.0848
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -4.542776 4.783003 Inf -0.950 0.3422
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -2.567071 7.747155 Inf -0.331 0.7404
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -7.125282 7.744893 Inf -0.920 0.3576
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 4.757892 5.701255 Inf 0.835 0.4040
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 5.880923 5.437575 Inf 1.082 0.2795
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## 16.581112 7.721617 Inf 2.147 0.0318
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df z.ratio p.value
## -12.743366 7.617350 Inf -1.673 0.0943
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
confint(contrast(lsmeans(modelPASE_8, ~Pandemic|timefactor|Age_sex), "pairwise", adj="none"), parm, level = 0.95)
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## timefactor = 1, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 8.821770 5.118474 Inf -1.210254 18.85379
##
## timefactor = 2, Age_sex = Females 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -4.542776 4.783003 Inf -13.917289 4.83174
##
## timefactor = 1, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -2.567071 7.747155 Inf -17.751216 12.61707
##
## timefactor = 2, Age_sex = Females 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -7.125282 7.744893 Inf -22.304992 8.05443
##
## timefactor = 1, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 4.757892 5.701255 Inf -6.416363 15.93215
##
## timefactor = 2, Age_sex = Males 45-64:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 5.880923 5.437575 Inf -4.776527 16.53837
##
## timefactor = 1, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## 16.581112 7.721617 Inf 1.447022 31.71520
##
## timefactor = 2, Age_sex = Males 65+:
## contrast
## (FU2 data collected after COVID-19) - (FU2 data collected before COVID-19)
## estimate SE df asymp.LCL asymp.UCL
## -12.743366 7.617350 Inf -27.673097 2.18637
##
## Results are averaged over the levels of: Education, Ethnicity, IncomeLevel, SmokingStatus, Relationshipstatus, Livingstatus, Anxiety, MoodDisord
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
PASE_lsmeans_8 <- summary(lsmeans(modelPASE_8, ~timefactor|Pandemic|Age_sex))
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3125' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3125)' or larger];
## but be warned that this may result in large computation time and memory use.
PASE_lsmeans_8$Time<-NA
PASE_lsmeans_8$Time[PASE_lsmeans_8$timefactor==1]<-"Follow-up 1"
PASE_lsmeans_8$Time[PASE_lsmeans_8$timefactor==2]<-"Follow-up 2"
ggplot(PASE_lsmeans_8, aes(x = Time, y = lsmean, fill = Pandemic)) +
geom_bar(stat="identity", position=position_dodge()) + facet_wrap(~Age_sex, scales = "free") +
labs(x = "Time", y = "PASE Total Score", title = "PASE Total Score from FU1 to FU2 by Pandemic status
(controlling for baseline)") +
theme_bw()
Create binary variable for 4+ hrs/day of SB
truncated.data_long_2$SB.binary <- as.factor(ifelse(truncated.data_long_2$PASE_Sit==10, 1, 0))
truncated.data_long_2$SBbaseline.binary <- as.factor(ifelse(truncated.data_long_2$PASE_Sitbaseline==10, 1, 0))
sit3 <- glmer(
SB.binary ~ timefactor*Pandemic + Age + Sex + SBbaseline.binary + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
(1|ID),
data = truncated.data_long_2,
family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 5.82512 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?
sit3
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: SB.binary ~ timefactor * Pandemic + Age + Sex + SBbaseline.binary +
## Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +
## SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +
## MoodDisord + Chronicconditions + (1 | ID)
## Data: truncated.data_long_2
## AIC BIC logLik deviance df.resid
## 21003.89 21243.53 -10470.94 20941.89 16789
## Random effects:
## Groups Name Std.Dev.
## ID (Intercept) 0.8871
## Number of obs: 16820, groups: ID, 8439
## Fixed Effects:
## (Intercept)
## -2.43888
## timefactor2
## 0.49340
## PandemicFU2 data collected before COVID-19
## 0.01055
## Age
## 0.01880
## SexM
## -0.13407
## SBbaseline.binary1
## 1.10919
## EducationHigh School Diploma
## 0.02148
## EducationLess than High School Diploma
## 0.04818
## EducationSome College
## -0.04292
## EthnicityWhite
## -0.16834
## IncomeLevel>$150k
## -0.18740
## IncomeLevel$100-150k
## 0.06256
## IncomeLevel$20-50k
## -0.17761
## IncomeLevel$50-100k
## -0.23650
## BMI
## 0.04201
## CESD.10baseline
## 0.01536
## SmokingStatusFormer Smoker
## -0.33779
## SmokingStatusNever Smoked
## -0.33952
## SmokingStatusOccasional Smoker
## -0.28201
## RelationshipstatusMarried
## -0.20546
## RelationshipstatusSeparated
## -0.05836
## RelationshipstatusSingle
## 0.04569
## RelationshipstatusWidowed
## -0.03352
## LivingstatusAssisted Living
## 0.10082
## LivingstatusHouse
## -0.28383
## LivingstatusOther
## -0.63877
## AnxietyYes
## 0.01032
## MoodDisordYes
## 0.07900
## Chronicconditions
## 0.02677
## timefactor2:PandemicFU2 data collected before COVID-19
## -0.25441
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 3 lme4 warnings
ggpredict(sit3, c("timefactor", "Pandemic"))
## # Predicted probabilities of SB.binary
##
## # Pandemic = FU2 data collected after COVID-19
##
## timefactor | Predicted | 95% CI
## -------------------------------------
## 1 | 0.50 | [0.41, 0.58]
## 2 | 0.62 | [0.53, 0.70]
##
## # Pandemic = FU2 data collected before COVID-19
##
## timefactor | Predicted | 95% CI
## -------------------------------------
## 1 | 0.50 | [0.42, 0.59]
## 2 | 0.56 | [0.47, 0.64]
##
## Adjusted for:
## * Age = 60.00
## * Sex = F
## * SBbaseline.binary = 0
## * Education = College Degree or Higher
## * Ethnicity = Other
## * IncomeLevel = <$20k
## * BMI = 27.50
## * CESD.10baseline = 4.88
## * SmokingStatus = Daily Smoker
## * Relationshipstatus = Divorced
## * Livingstatus = Apartment/Condo/Townhome
## * Anxiety = No
## * MoodDisord = No
## * Chronicconditions = 2.71
## * ID = 0 (population-level)
Mean Differences and 95% CIs
sit3.test <- as.data.frame(ggpredict(sit3, c("timefactor", "Pandemic")))
mean.diff.5<-(subset(sit3.test,x==1 & group == "FU2 data collected after COVID-19")$predicted -
subset(sit3.test,x==1 & group == "FU2 data collected before COVID-19")$predicted)
se.5<-(sqrt(((subset(sit3.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sit3.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
UL.5<-mean.diff.5 + se.5*1.96
LL.5<-mean.diff.5 - se.5*1.96
mean.diff.6<-(subset(sit3.test,x==2 & group == "FU2 data collected after COVID-19")$predicted -
subset(sit3.test,x==2 & group == "FU2 data collected before COVID-19")$predicted)
se.6<-(sqrt(((subset(sit3.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sit3.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
UL.6<-mean.diff.6 + se.6*1.96
LL.6<-mean.diff.6 - se.6*1.96
#Mean differences and LL and UL
mean.diff.5
## [1] -0.002636852
UL.5
## [1] 0.3433034
LL.5
## [1] -0.3485771
mean.diff.6
## [1] 0.05892594
UL.6
## [1] 0.405127
LL.6
## [1] -0.2872752
Z-Scores
sit3.test <- as.data.frame(ggpredict(sit3, c("timefactor", "Pandemic")))
z.5<- (subset(sit3.test,x==1 & group == "FU2 data collected after COVID-19")$predicted -
subset(sit3.test,x==1 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sit3.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sit3.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
z.6<- (subset(sit3.test,x==2 & group == "FU2 data collected after COVID-19")$predicted -
subset(sit3.test,x==2 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sit3.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sit3.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
#z-scores
z.5
## [1] -0.01493966
z.6
## [1] 0.3336062
p-values for z-scores
2*pnorm(z.5, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 1.01192
2*pnorm(z.6, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.7386767
ggpredict(sit3, c("timefactor", "Pandemic")) %>% plot()
sit4 <- glmer(
SB.binary ~ timefactor*Pandemic*Age_sex + SBbaseline.binary + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
(1|ID),
data = truncated.data_long_2,
family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.229753 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
sit2
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: SB.binary ~ timefactor * Pandemic * Age_sex + SBbaseline.binary +
## Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +
## SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +
## MoodDisord + Chronicconditions + (1 | ID)
## Data: Tracking.data_long_2
## AIC BIC logLik deviance df.resid
## 25497.29 25822.13 -12707.64 25415.29 20352
## Random effects:
## Groups Name Std.Dev.
## ID (Intercept) 0.901
## Number of obs: 20393, groups: ID, 10230
## Fixed Effects:
## (Intercept)
## -1.620686
## timefactor2
## 0.580652
## PandemicFU2 data collected before COVID-19
## 0.057124
## Age_sexFemales 65+
## 0.398934
## Age_sexMales 45-64
## -0.146265
## Age_sexMales 65+
## 0.303371
## SBbaseline.binary1
## 1.150707
## EducationHigh School Diploma
## -0.005042
## EducationLess than High School Diploma
## 0.096709
## EducationSome College
## -0.073105
## EthnicityWhite
## -0.054078
## IncomeLevel>$150k
## -0.206336
## IncomeLevel$100-150k
## -0.032560
## IncomeLevel$20-50k
## -0.162937
## IncomeLevel$50-100k
## -0.243375
## BMI
## 0.039126
## CESD.10baseline
## 0.011728
## SmokingStatusFormer Smoker
## -0.290040
## SmokingStatusNever Smoked
## -0.315386
## SmokingStatusOccasional Smoker
## -0.327601
## RelationshipstatusMarried
## -0.176033
## RelationshipstatusSeparated
## 0.061149
## RelationshipstatusSingle
## 0.068453
## RelationshipstatusWidowed
## 0.044016
## LivingstatusAssisted Living
## 0.309546
## LivingstatusHouse
## -0.239899
## LivingstatusOther
## -0.451692
## AnxietyYes
## -0.039052
## MoodDisordYes
## 0.112731
## Chronicconditions
## 0.042309
## timefactor2:PandemicFU2 data collected before COVID-19
## -0.342007
## timefactor2:Age_sexFemales 65+
## -0.141938
## timefactor2:Age_sexMales 45-64
## -0.060952
## timefactor2:Age_sexMales 65+
## -0.124570
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## -0.015392
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## 0.104367
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## 0.094448
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## -0.173610
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## -0.037797
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## -0.387304
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 2 lme4 warnings
ggpredict(sit4, c("Pandemic","timefactor","Age_sex"))
## # Predicted probabilities of SB.binary
##
## # timefactor = 1
## # Age_sex = Females 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.43 | [0.34, 0.52]
## FU2 data collected before COVID-19 | 0.42 | [0.34, 0.51]
##
## # timefactor = 2
## # Age_sex = Females 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.57 | [0.48, 0.66]
## FU2 data collected before COVID-19 | 0.53 | [0.44, 0.62]
##
## # timefactor = 1
## # Age_sex = Females 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.54 | [0.44, 0.63]
## FU2 data collected before COVID-19 | 0.52 | [0.43, 0.62]
##
## # timefactor = 2
## # Age_sex = Females 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.64 | [0.55, 0.72]
## FU2 data collected before COVID-19 | 0.55 | [0.46, 0.64]
##
## # timefactor = 1
## # Age_sex = Males 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.39 | [0.31, 0.48]
## FU2 data collected before COVID-19 | 0.43 | [0.34, 0.53]
##
## # timefactor = 2
## # Age_sex = Males 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.52 | [0.43, 0.61]
## FU2 data collected before COVID-19 | 0.48 | [0.39, 0.58]
##
## # timefactor = 1
## # Age_sex = Males 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.51 | [0.41, 0.61]
## FU2 data collected before COVID-19 | 0.53 | [0.43, 0.62]
##
## # timefactor = 2
## # Age_sex = Males 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.62 | [0.53, 0.71]
## FU2 data collected before COVID-19 | 0.51 | [0.41, 0.61]
##
## Adjusted for:
## * SBbaseline.binary = 0
## * Education = College Degree or Higher
## * Ethnicity = Other
## * IncomeLevel = <$20k
## * BMI = 27.50
## * CESD.10baseline = 4.88
## * SmokingStatus = Daily Smoker
## * Relationshipstatus = Divorced
## * Livingstatus = Apartment/Condo/Townhome
## * Anxiety = No
## * MoodDisord = No
## * Chronicconditions = 2.71
## * ID = 0 (population-level)
Mean differences and 95% CIs
sit.test4 <- as.data.frame(ggpredict(sit4, c("timefactor", "Pandemic", "Age_sex")))
#Females 45-64 years
mean.diff.Females.Young.5<-(subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
se.Females.Young.5<-(sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
UL.Females.Young.5<-mean.diff.Females.Young.5 + se.Females.Young.5*1.96
LL.Females.Young.5<-mean.diff.Females.Young.5 - se.Females.Young.5*1.96
mean.diff.Females.Young.6<-(subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
se.Females.Young.6<-(sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
UL.Females.Young.6<-mean.diff.Females.Young.6 + se.Females.Young.6*1.96
LL.Females.Young.6<-mean.diff.Females.Young.6 - se.Females.Young.6*1.96
#Females 65+ years
mean.diff.Females.Old.5<-(subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
se.Females.Old.5<-(sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
UL.Females.Old.5<-mean.diff.Females.Old.5 + se.Females.Old.5*1.96
LL.Females.Old.5<-mean.diff.Females.Old.5 - se.Females.Old.5*1.96
mean.diff.Females.Old.6<-(subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
se.Females.Old.6<-(sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
UL.Females.Old.6<-mean.diff.Females.Old.6 + se.Females.Old.6*1.96
LL.Females.Old.6<-mean.diff.Females.Old.6 - se.Females.Old.6*1.96
#Males 45-64 years
mean.diff.Males.Young.5<-(subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
se.Males.Young.5<-(sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
UL.Males.Young.5<-mean.diff.Males.Young.5 + se.Males.Young.5*1.96
LL.Males.Young.5<-mean.diff.Males.Young.5 - se.Males.Young.5*1.96
mean.diff.Males.Young.6<-(subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
se.Males.Young.6<-(sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
UL.Males.Young.6<-mean.diff.Males.Young.6 + se.Males.Young.6*1.96
LL.Males.Young.6<-mean.diff.Males.Young.6 - se.Males.Young.6*1.96
#Males 65+ years
mean.diff.Males.Old.5<-(subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
se.Males.Old.5<-(sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
UL.Males.Old.5<-mean.diff.Males.Old.5 + se.Males.Old.5*1.96
LL.Males.Old.5<-mean.diff.Males.Old.5 - se.Males.Old.5*1.96
mean.diff.Males.Old.6<-(subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
se.Males.Old.6<-(sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
UL.Males.Old.6<-mean.diff.Males.Old.6 + se.Males.Old.6*1.96
LL.Males.Old.6<-mean.diff.Males.Old.6 - se.Males.Old.6*1.96
#Mean differences and LL and UL
mean.diff.Females.Young.5
## [1] 0.004546467
UL.Females.Young.5
## [1] 0.3675635
LL.Females.Young.5
## [1] -0.3584706
mean.diff.Females.Young.6
## [1] 0.04191875
UL.Females.Young.6
## [1] 0.4037047
LL.Females.Young.6
## [1] -0.3198672
mean.diff.Females.Old.5
## [1] 0.01323892
UL.Females.Old.5
## [1] 0.3971556
LL.Females.Old.5
## [1] -0.3706777
mean.diff.Females.Old.6
## [1] 0.08643343
UL.Females.Old.6
## [1] 0.4707015
LL.Females.Old.6
## [1] -0.2978347
mean.diff.Males.Young.5
## [1] -0.04130713
UL.Males.Young.5
## [1] 0.330242
LL.Males.Young.5
## [1] -0.4128563
mean.diff.Males.Young.6
## [1] 0.03797661
UL.Males.Young.6
## [1] 0.4084541
LL.Males.Young.6
## [1] -0.3325009
mean.diff.Males.Old.5
## [1] -0.01664062
UL.Males.Old.5
## [1] 0.374
LL.Males.Old.5
## [1] -0.4072812
mean.diff.Males.Old.6
## [1] 0.1114892
UL.Males.Old.6
## [1] 0.5022129
LL.Males.Old.6
## [1] -0.2792345
Z-scores
z.Females.Young.5 <- (subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
z.Females.Young.6 <- (subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
z.Females.Old.5 <- (subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
z.Females.Old.6 <- (subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
z.Males.Young.5 <- (subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
z.Males.Young.6 <- (subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
z.Males.Old.5 <- (subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sit.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sit.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
z.Males.Old.6 <- (subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sit.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sit.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
#z-scores
z.Females.Young.5
## [1] 0.02454727
z.Females.Young.6
## [1] 0.2270977
z.Males.Young.5
## [1] -0.2179038
z.Males.Young.6
## [1] 0.2009141
z.Females.Old.5
## [1] 0.06758834
z.Females.Old.6
## [1] 0.4408628
z.Males.Old.5
## [1] -0.08349261
z.Males.Old.6
## [1] 0.5592669
p-values for z-scores
2*pnorm(z.Females.Young.5, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.9804161
2*pnorm(z.Females.Young.6, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.8203478
2*pnorm(z.Males.Young.5, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 1.172496
2*pnorm(z.Males.Young.6, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.8407657
2*pnorm(z.Females.Old.5, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.9461133
2*pnorm(z.Females.Old.6, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.6593123
2*pnorm(z.Males.Old.5, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 1.06654
2*pnorm(z.Males.Old.6, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.5759796
ggpredict(sit4, c("timefactor","Pandemic","Age_sex")) %>% plot()
sleep3 <- glmer(
RSTLS_Sleep ~ timefactor*Pandemic + Age + Sex + RSTLS_Sleepbaseline + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
(1|ID),
data = truncated.data_long_2,
family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 3.52462 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?
sleep3
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## RSTLS_Sleep ~ timefactor * Pandemic + Age + Sex + RSTLS_Sleepbaseline +
## Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +
## SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +
## MoodDisord + Chronicconditions + (1 | ID)
## Data: truncated.data_long_2
## AIC BIC logLik deviance df.resid
## 20204.61 20444.91 -10071.30 20142.61 17150
## Random effects:
## Groups Name Std.Dev.
## ID (Intercept) 0.9005
## Number of obs: 17181, groups: ID, 8611
## Fixed Effects:
## (Intercept)
## -1.0418064
## timefactor2
## -0.1945906
## PandemicFU2 data collected before COVID-19
## -0.0515297
## Age
## -0.0119102
## SexM
## -0.2589129
## RSTLS_Sleepbaseline
## 0.9497744
## EducationHigh School Diploma
## 0.1890673
## EducationLess than High School Diploma
## 0.2068022
## EducationSome College
## 0.1085545
## EthnicityWhite
## 0.1331178
## IncomeLevel>$150k
## -0.0381059
## IncomeLevel$100-150k
## 0.0865390
## IncomeLevel$20-50k
## -0.0187249
## IncomeLevel$50-100k
## 0.0363318
## BMI
## -0.0003967
## CESD.10baseline
## 0.0632756
## SmokingStatusFormer Smoker
## 0.0443048
## SmokingStatusNever Smoked
## -0.0658098
## SmokingStatusOccasional Smoker
## -0.0235334
## RelationshipstatusMarried
## -0.0057746
## RelationshipstatusSeparated
## -0.4337311
## RelationshipstatusSingle
## -0.1615102
## RelationshipstatusWidowed
## -0.1852212
## LivingstatusAssisted Living
## 0.1545573
## LivingstatusHouse
## 0.1093385
## LivingstatusOther
## 0.0254967
## AnxietyYes
## -0.1289796
## MoodDisordYes
## -0.0165015
## Chronicconditions
## 0.0909348
## timefactor2:PandemicFU2 data collected before COVID-19
## 0.0442628
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 3 lme4 warnings
ggpredict(sleep3, c("timefactor", "Pandemic"))
## # Predicted probabilities of RSTLS_Sleep
##
## # Pandemic = FU2 data collected after COVID-19
##
## timefactor | Predicted | 95% CI
## -------------------------------------
## 1 | 0.29 | [0.22, 0.37]
## 2 | 0.25 | [0.19, 0.32]
##
## # Pandemic = FU2 data collected before COVID-19
##
## timefactor | Predicted | 95% CI
## -------------------------------------
## 1 | 0.28 | [0.21, 0.36]
## 2 | 0.25 | [0.19, 0.32]
##
## Adjusted for:
## * Age = 60.00
## * Sex = F
## * RSTLS_Sleepbaseline = 0.33
## * Education = College Degree or Higher
## * Ethnicity = Other
## * IncomeLevel = <$20k
## * BMI = 27.51
## * CESD.10baseline = 4.90
## * SmokingStatus = Daily Smoker
## * Relationshipstatus = Divorced
## * Livingstatus = Apartment/Condo/Townhome
## * Anxiety = No
## * MoodDisord = No
## * Chronicconditions = 2.71
## * ID = 0 (population-level)
Mean Differences and 95% CIs
sleep3.test <- as.data.frame(ggpredict(sleep3, c("timefactor", "Pandemic")))
mean.diff.7<-(subset(sleep3.test,x==1 & group == "FU2 data collected after COVID-19")$predicted -
subset(sleep3.test,x==1 & group == "FU2 data collected before COVID-19")$predicted)
se.7<-(sqrt(((subset(sleep3.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sleep3.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
UL.7<-mean.diff.7 + se.7*1.96
LL.7<-mean.diff.7 - se.7*1.96
mean.diff.8<-(subset(sleep3.test,x==2 & group == "FU2 data collected after COVID-19")$predicted -
subset(sleep3.test,x==2 & group == "FU2 data collected before COVID-19")$predicted)
se.8<-(sqrt(((subset(sleep3.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sleep3.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
UL.8<-mean.diff.8 + se.6*1.96
LL.8<-mean.diff.8 - se.6*1.96
#Mean differences and LL and UL
mean.diff.7
## [1] 0.01047513
UL.7
## [1] 0.3659816
LL.7
## [1] -0.3450313
mean.diff.8
## [1] 0.001363091
UL.8
## [1] 0.3475642
LL.8
## [1] -0.344838
sleep3.test <- as.data.frame(ggpredict(sleep3, c("timefactor", "Pandemic")))
z.7<- (subset(sleep3.test,x==1 & group == "FU2 data collected after COVID-19")$predicted -
subset(sleep3.test,x==1 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sleep3.test,x==1 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sleep3.test,x==1 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
z.8<- (subset(sleep3.test,x==2 & group == "FU2 data collected after COVID-19")$predicted -
subset(sleep3.test,x==2 & group == "FU2 data collected before COVID-19")$predicted) / (sqrt(((subset(sleep3.test,x==2 & group == "FU2 data collected after COVID-19")$std.error)^2 +
(subset(sleep3.test,x==2 & group == "FU2 data collected before COVID-19")$std.error^2))/2))
#z-scores
z.7
## [1] 0.05775215
z.8
## [1] 0.007501955
p-values for z-scores
2*pnorm(z.7, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.9539461
2*pnorm(z.8, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.9940144
ggpredict(sleep3, c("timefactor", "Pandemic")) %>% plot()
sleep4 <- glmer(
RSTLS_Sleep ~ timefactor*Pandemic*Age_sex + RSTLS_Sleepbaseline + Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline + SmokingStatus +
Relationshipstatus + Livingstatus + Anxiety + MoodDisord + Chronicconditions +
(1|ID),
data = truncated.data_long_2,
family = binomial(link = "logit")
)
## Warning in (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, :
## failure to converge in 10000 evaluations
## Warning in optwrap(optimizer, devfun, start, rho$lower, control = control, :
## convergence code 4 from Nelder_Mead: failure to converge in 10000 evaluations
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 4.71501 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
sleep4
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: RSTLS_Sleep ~ timefactor * Pandemic * Age_sex + RSTLS_Sleepbaseline +
## Education + Ethnicity + IncomeLevel + BMI + CESD.10baseline +
## SmokingStatus + Relationshipstatus + Livingstatus + Anxiety +
## MoodDisord + Chronicconditions + (1 | ID)
## Data: truncated.data_long_2
## AIC BIC logLik deviance df.resid
## 20201.13 20518.95 -10059.57 20119.13 17140
## Random effects:
## Groups Name Std.Dev.
## ID (Intercept) 0.9069
## Number of obs: 17181, groups: ID, 8611
## Fixed Effects:
## (Intercept)
## -1.6343390
## timefactor2
## -0.2754714
## PandemicFU2 data collected before COVID-19
## -0.1104027
## Age_sexFemales 65+
## -0.2002653
## Age_sexMales 45-64
## -0.5172539
## Age_sexMales 65+
## -0.4712077
## RSTLS_Sleepbaseline
## 0.9546104
## EducationHigh School Diploma
## 0.1797473
## EducationLess than High School Diploma
## 0.2136112
## EducationSome College
## 0.1332032
## EthnicityWhite
## 0.1308369
## IncomeLevel>$150k
## -0.0100607
## IncomeLevel$100-150k
## 0.1190651
## IncomeLevel$20-50k
## -0.0225508
## IncomeLevel$50-100k
## 0.0418234
## BMI
## 0.0009517
## CESD.10baseline
## 0.0635611
## SmokingStatusFormer Smoker
## 0.0340065
## SmokingStatusNever Smoked
## -0.0633799
## SmokingStatusOccasional Smoker
## -0.0628344
## RelationshipstatusMarried
## -0.0255264
## RelationshipstatusSeparated
## -0.4013104
## RelationshipstatusSingle
## -0.1510180
## RelationshipstatusWidowed
## -0.2338176
## LivingstatusAssisted Living
## 0.1620799
## LivingstatusHouse
## 0.1285654
## LivingstatusOther
## -0.0917031
## AnxietyYes
## -0.1349351
## MoodDisordYes
## -0.0118410
## Chronicconditions
## 0.0886673
## timefactor2:PandemicFU2 data collected before COVID-19
## 0.1061261
## timefactor2:Age_sexFemales 65+
## 0.0848093
## timefactor2:Age_sexMales 45-64
## 0.2520917
## timefactor2:Age_sexMales 65+
## -0.1858191
## PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## -0.2228146
## PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## 0.3026941
## PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## 0.0524889
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexFemales 65+
## -0.1033567
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 45-64
## -0.2996806
## timefactor2:PandemicFU2 data collected before COVID-19:Age_sexMales 65+
## 0.3004752
## optimizer (Nelder_Mead) convergence code: 4 (failure to converge in 10000 evaluations) ; 1 optimizer warnings; 2 lme4 warnings
ggpredict(sleep4, c("Pandemic","timefactor","Age_sex"))
## # Predicted probabilities of RSTLS_Sleep
##
## # timefactor = 1
## # Age_sex = Females 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.32 | [0.25, 0.41]
## FU2 data collected before COVID-19 | 0.30 | [0.23, 0.38]
##
## # timefactor = 2
## # Age_sex = Females 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.27 | [0.20, 0.34]
## FU2 data collected before COVID-19 | 0.26 | [0.20, 0.34]
##
## # timefactor = 1
## # Age_sex = Females 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.28 | [0.21, 0.37]
## FU2 data collected before COVID-19 | 0.22 | [0.16, 0.29]
##
## # timefactor = 2
## # Age_sex = Females 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.24 | [0.18, 0.32]
## FU2 data collected before COVID-19 | 0.19 | [0.13, 0.26]
##
## # timefactor = 1
## # Age_sex = Males 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.22 | [0.16, 0.29]
## FU2 data collected before COVID-19 | 0.26 | [0.19, 0.34]
##
## # timefactor = 2
## # Age_sex = Males 45-64
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.22 | [0.16, 0.29]
## FU2 data collected before COVID-19 | 0.22 | [0.16, 0.29]
##
## # timefactor = 1
## # Age_sex = Males 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.23 | [0.17, 0.31]
## FU2 data collected before COVID-19 | 0.22 | [0.16, 0.30]
##
## # timefactor = 2
## # Age_sex = Males 65+
##
## Pandemic | Predicted | 95% CI
## -------------------------------------------------------------
## FU2 data collected after COVID-19 | 0.16 | [0.11, 0.22]
## FU2 data collected before COVID-19 | 0.21 | [0.15, 0.28]
##
## Adjusted for:
## * RSTLS_Sleepbaseline = 0.33
## * Education = College Degree or Higher
## * Ethnicity = Other
## * IncomeLevel = <$20k
## * BMI = 27.51
## * CESD.10baseline = 4.90
## * SmokingStatus = Daily Smoker
## * Relationshipstatus = Divorced
## * Livingstatus = Apartment/Condo/Townhome
## * Anxiety = No
## * MoodDisord = No
## * Chronicconditions = 2.71
## * ID = 0 (population-level)
Mean differences and 95% CIs
sleep.test4 <- as.data.frame(ggpredict(sleep4, c("timefactor", "Pandemic", "Age_sex")))
#Females 45-64 years
mean.diff.Females.Young.7<-(subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
se.Females.Young.7<-(sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
UL.Females.Young.7<-mean.diff.Females.Young.7 + se.Females.Young.7*1.96
LL.Females.Young.7<-mean.diff.Females.Young.7 - se.Females.Young.7*1.96
mean.diff.Females.Young.8<-(subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted)
se.Females.Young.8<-(sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
UL.Females.Young.8<-mean.diff.Females.Young.8 + se.Females.Young.8*1.96
LL.Females.Young.8<-mean.diff.Females.Young.8 - se.Females.Young.8*1.96
#Females 65+ years
mean.diff.Females.Old.7<-(subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
se.Females.Old.7<-(sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
UL.Females.Old.7<-mean.diff.Females.Old.7 + se.Females.Old.7*1.96
LL.Females.Old.7<-mean.diff.Females.Old.7 - se.Females.Old.7*1.96
mean.diff.Females.Old.8<-(subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted)
se.Females.Old.8<-(sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
UL.Females.Old.8<-mean.diff.Females.Old.8 + se.Females.Old.8*1.96
LL.Females.Old.8<-mean.diff.Females.Old.8 - se.Females.Old.8*1.96
#Males 45-64 years
mean.diff.Males.Young.7<-(subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
se.Males.Young.7<-(sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
UL.Males.Young.7<-mean.diff.Males.Young.7 + se.Males.Young.7*1.96
LL.Males.Young.7<-mean.diff.Males.Young.7 - se.Males.Young.7*1.96
mean.diff.Males.Young.8<-(subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted)
se.Males.Young.8<-(sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
UL.Males.Young.8<-mean.diff.Males.Young.8 + se.Males.Young.8*1.96
LL.Males.Young.8<-mean.diff.Males.Young.8 - se.Males.Young.8*1.96
#Males 65+ years
mean.diff.Males.Old.7<-(subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
se.Males.Old.7<-(sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
UL.Males.Old.7<-mean.diff.Males.Old.7 + se.Males.Old.7*1.96
LL.Males.Old.7<-mean.diff.Males.Old.7 - se.Males.Old.7*1.96
mean.diff.Males.Old.8<-(subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted)
se.Males.Old.8<-(sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
UL.Males.Old.8<-mean.diff.Males.Old.8 + se.Males.Old.8*1.96
LL.Males.Old.8<-mean.diff.Males.Old.8 - se.Males.Old.8*1.96
#Mean differences and LL and UL
mean.diff.Females.Young.7
## [1] 0.02362387
UL.Females.Young.7
## [1] 0.3940529
LL.Females.Young.7
## [1] -0.3468051
mean.diff.Females.Young.8
## [1] 0.0008325664
UL.Females.Young.8
## [1] 0.3723871
LL.Females.Young.8
## [1] -0.370722
mean.diff.Females.Old.7
## [1] 0.06207411
UL.Females.Old.7
## [1] 0.4573061
LL.Females.Old.7
## [1] -0.3331579
mean.diff.Females.Old.8
## [1] 0.05562014
UL.Females.Old.8
## [1] 0.452915
LL.Females.Old.8
## [1] -0.3416747
mean.diff.Males.Young.7
## [1] -0.03485029
UL.Males.Young.7
## [1] 0.3462642
LL.Males.Young.7
## [1] -0.4159648
mean.diff.Males.Young.8
## [1] 0.0002144097
UL.Males.Young.8
## [1] 0.3825704
LL.Males.Young.8
## [1] -0.3821416
mean.diff.Males.Old.7
## [1] 0.01005995
UL.Males.Old.7
## [1] 0.4134149
LL.Males.Old.7
## [1] -0.393295
mean.diff.Males.Old.8
## [1] -0.05197637
UL.Males.Old.8
## [1] 0.3554982
LL.Males.Old.8
## [1] -0.4594509
Z-scores
z.Females.Young.7 <- (subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
z.Females.Young.8 <- (subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$predicted -
subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$predicted) / (sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 45-64")$std.error)^2 +
(subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 45-64")$std.error^2))/2))
z.Females.Old.7 <- (subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
z.Females.Old.8 <- (subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$predicted -
subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$predicted) / (sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Females 65+")$std.error)^2 +
(subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Females 65+")$std.error^2))/2))
z.Males.Young.7 <- (subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
z.Males.Young.8 <- (subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$predicted -
subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$predicted) / (sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 45-64")$std.error)^2 +
(subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 45-64")$std.error^2))/2))
z.Males.Old.7 <- (subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sleep.test4,x==1 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sleep.test4,x==1 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
z.Males.Old.8 <- (subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$predicted -
subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$predicted) / (sqrt(((subset(sleep.test4,x==2 & group == "FU2 data collected after COVID-19" & facet == "Males 65+")$std.error)^2 +
(subset(sleep.test4,x==2 & group == "FU2 data collected before COVID-19" & facet == "Males 65+")$std.error^2))/2))
#z-scores
z.Females.Young.7
## [1] 0.1249977
z.Females.Young.8
## [1] 0.004391899
z.Males.Young.7
## [1] -0.1792285
z.Males.Young.8
## [1] 0.001099088
z.Females.Old.7
## [1] 0.3078325
z.Females.Old.8
## [1] 0.2743944
z.Males.Old.7
## [1] 0.04888373
z.Males.Old.8
## [1] -0.2500124
p-values for z-scores
2*pnorm(z.Females.Young.7, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.9005253
2*pnorm(z.Females.Young.8, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.9964958
2*pnorm(z.Males.Young.7, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 1.142242
2*pnorm(z.Males.Young.8, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.9991231
2*pnorm(z.Females.Old.7, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.7582098
2*pnorm(z.Females.Old.8, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.7837816
2*pnorm(z.Males.Old.7, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 0.961012
2*pnorm(z.Males.Old.8, mean = 0, sd = 1, lower.tail = FALSE)
## [1] 1.197422
ggpredict(sleep4, c("timefactor","Pandemic","Age_sex")) %>% plot()